Search: “monocular-depth”

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Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation

Marigold V2 adapts diffusion transformers for monocular depth estimation, improving AbsRel 16-26% over the previous best on KITTI and ETH3D.

Huawei's Bayer lab revisits the Marigold approach to repurpose image generation and editing models built on the diffusion transformer (DiT) architecture into monocular depth estimators. The recipes target single-step inference from pretrained multi-step flow-matching models, with remedies including alignment to ground-truth semantic features and a two-stage fine-tuning protocol using a Sinkhorn-based loss. The resulting model produces crisper depth maps that generalize out-of-distribution and also achieves state-of-the-art results on surface normals estimation and intrinsic image decomposition.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

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TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation

TransNormal-2 improves monocular surface-normal estimation by fixing VAE edge degradation with geometry-aware losses and refinement, matching MoGe-2 with 1.4% of annotations.

TransNormal-2 is a FLUX.2-based rectified-flow framework for monocular surface-normal estimation with single-step deterministic inference. The authors quantify that VAE 8x spatial compression introduces 1.3-8.5 degrees of mean angular error even on ground-truth normals, with edge error up to 2.8x the global error. The method adds geometry-aware pixel-space losses and an RGB-guided Geometric Refinement Module to correct boundary-localized decoding errors. It matches or exceeds MoGe-2 on all eight reported metrics using only 1.4% as many task-specific annotations, and cuts transparent-object MAE by 4.2 degrees on ClearGrasp and 3.1 degrees on ClearPose.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

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Illusion of Depth: Revealing Hidden Stereo Vision Vulnerabilities in Depth Estimation

Simple repeating patterns let attackers shift stereo-camera depth estimates by up to 20 meters, triggering emergency braking in autonomous driving frameworks at 40 km/h.

The paper reveals an intrinsic vulnerability in stereo cameras stemming from pixel sampling and calibration processes, letting attackers finely control estimated depth of real obstacles using simple repeating patterns without adversarial ML techniques. The attack was evaluated against BM and SGBM stereo matching algorithms, deep learning models PSMNet, MoCha-Stereo, and UniMatch, the stereo-LiDAR fusion model SGM-DDC, and commercial cameras ZED2 and Intel RealSense D435; on ZED2, obstacles can be displaced up to 20 meters farther or 12 meters closer. A 0.5-second attack triggered emergency braking in a popular autonomous driving framework, with feasibility confirmed at driving speeds up to 40 km/h using CARLA. State-of-the-art defenses proved ineffective, and the authors propose a similarity-score strategy to dynamically detect and suppress depth discrepancies.

arXiv cs.CR·Sep 14, 20:51 UTC · 1d agoResearch

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AlayaVista: Streaming World Modeling from Panoramic States to Perspective Video

AlayaVista is a camera-controllable streaming video world model that decouples panoramic scene evolution from perspective synthesis, trained on a 1,318-hour 4K dataset.

AlayaVista builds a 360-degree scene prior from a single perspective image, evolves it as a camera-conditioned panoramic latent state, and maps it to perspective video via a latent viewport renderer plus a perspective refiner. Chunk-autoregressive generation and few-step distillation enable efficient streaming. The authors introduce MUGEN, a real-world panoramic video dataset with 1,318 hours of at-least-4K video and rich semantic and geometric annotations.

Hugging Face daily papers·Sep 12, 20:00 UTC · 3d agoAI research

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Recursive Code World Models: Building Complex Worlds through Recursive Scene Programs

RCWM reconstructs complex 3D worlds as executable code from a single image using recursive scene programs with global-local-global solver recursion.

The paper introduces Recursive Code World Models, coupling a Recursive Scene Program representation with a recursive construction solver for image-to-3D-world reconstruction. Each solver call establishes the whole scene, recursively reconstructs unresolved parts, and revisits the whole to refine composition, while a vision-language coding agent compares reference images with scene renders to guide refinement. RCWM outperforms prior code-based image-to-scene reconstruction methods, and ablations show deeper recursive calls improve fine-scale reconstruction.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research1

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RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting

RelightFormer is a feed-forward generative transformer for photorealistic single- and multi-view object relighting, trained on a 90K-object dataset.

Researchers introduce RelightFormer, a feed-forward generative transformer adapted from a video foundation model that performs direct image relighting without explicit intrinsic property estimation. The architecture injects target environment maps via a latent illumination module with cross-attention and uses permutation-invariant positional encodings for unordered multi-view inputs. Training relies on the newly constructed Laval Objaverse Dataset (LOD) with 90K objects and 39K unique illuminations, and the model shows state-of-the-art quality with strong zero-shot generalization across single-view, multi-view, and novel-view relighting.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

UniH3 unifies hierarchical homogeneity and heterogeneity modeling for all-in-one medical image restoration across modalities and degradation types.

UniH3 introduces a Hierarchical Homogeneity Memory module that distills shared anatomical priors from high-quality images, injected via a Homogeneity-Guided Attention mechanism. A Hierarchical Heterogeneity Balancer mitigates inter- and intra-task conflicts during multi-task optimization. It achieves state-of-the-art on MedIR-2D-500K and MedIR-3D-3D benchmarks for both all-in-one and single-task restoration, with code released on GitHub.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

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Hierarchical NeRF with JAX3D for Volumetric Rendering, Novel-View Synthesis, and 3D Reconstruction

MarkTechPost tutorial implements a hierarchical NeRF in JAX using jax3d volume-rendering primitives for novel-view synthesis and 3D reconstruction.

The tutorial builds an end-to-end hierarchical Neural Radiance Field using JAX, Flax, Optax, and jax3d's volume-rendering functions (sample_along_rays, volume_rendering, sample_piecewise_constant_pdf). It implements positional encoding, skip connections, separate coarse and fine networks, and view-direction conditioning with hierarchical importance sampling. Training uses JAX JIT compilation, Adam optimization, exponential learning-rate decay, and gradient clipping. Evaluation covers PSNR, depth and opacity visualization, 360-degree rendering, and marching-cubes geometry extraction.

MarkTechPost·Sep 13, 19:46 UTC · 2d agoAI research

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GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting

GoDeep achieves annotation-free open-vocabulary 3D segmentation by grounding structured image descriptions in language-only embeddings, outperforming CLIP-based lifting on out-of-vocabulary objects.

GoDeep uses a vision-language model purely as a translator, producing structured entity-level image descriptions that are grounded, projected, and aggregated in a general-purpose language-only embedding space, with no 3D training corpus or dedicated 3D encoder required. On ScanNet++ the pipeline is competitive with strong annotation-free baselines, and on a cultural-heritage benchmark a systematic vocabulary correction reverses initial CLIP-based rankings. Language-space embeddings separate genuinely out-of-vocabulary objects more sharply, localize them within scenes, and keep all predictions explainable as discrete text.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:31 UTC · 7d agoAI research

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OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution

OracleZoom enables recursive extreme-scale image super-resolution via reference-constrained on-policy distillation, reducing hallucinations at deep zoom scales.

OracleZoom tackles recursive super-resolution, where repeated feeding of predictions back into the same model leaves deeper-scale outputs unsupervised as required source resolution grows geometrically. The framework trains on its own trajectory while carrying the last ground-truth evidence beyond the supervision boundary, combining direct and cross-scale supervision, a no-reference quality objective, a KL-constrained pretrained latent prior, and EMA consistency. Across seven datasets it achieves state-of-the-art SR quality across zoom scales, averaging 0.713 CLIPIQA with larger gains at deeper scales and significantly reduced hallucinations. Code, data, and models are publicly released.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

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Reflection-aware Generative Novel View Synthesis

Ref-GeNVS is a training-free method for reflection-consistent generative novel view synthesis that treats mirror images as two complementary views.

An arXiv paper proposes Ref-GeNVS, a training-free approach to generative novel view synthesis in scenes containing mirrors. It estimates the mirror plane, reflects camera poses to create virtual views, and applies mirror-gated attention plus reflection injection within a multi-view diffusion model. On synthetic and real mirror scenes, Ref-GeNVS outperforms recent generative NVS methods while requiring no fine-tuning.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:33 UTC · 11d agoAI research

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FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

FreeFlow is a bias-free hierarchical transformer achieving state-of-the-art optical flow results on Sintel, KITTI-2015, and Spring benchmarks.

FreeFlow replaces task-specific inductive biases like correlation volumes and iterative warping with a single feed-forward encoder-decoder combining window, shifted-window, and reduced-resolution global attention. It reaches 0.68/1.48 EPE on Sintel Clean/Final, 3.23 Fl-all on KITTI-2015, and 3.192 1px on Spring. The architecture scales consistently from small to large variants and remains memory efficient at 1080p inference.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

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WarmBloodAban/Minimax-h3_Singularity — new model trending #22 on Hugging Face

Community fine-tune Minimax-h3_Singularity enhances MiniMax-H3 video generation with HDR quality, distant face restoration, and improved motion, trending #22 on Hugging Face.

Minimax-h3_Singularity is a community fusion fine-tune of the MiniMax-H3 multimodal video generation model, built from multiple checkpoints and refined with pruning and weight optimization. It supports Text-to-Video, Image-to-Video, Reference-to-Video, and Video-to-Video workflows in ComfyUI, and claims improvements in HDR clarity, distant face restoration, motion fluidity, and fantasy VFX. The authors recommend pairing it with the minimax_h3_ref2v_turbo_4step_v0.1 LoRA for four-step accelerated inference, and an online demo is available via RunningHub.

Hugging Face trending models·Sep 5, 18:51 UTC · 10d agoModel release7· 1 read

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Kaininja: Extending Native 3D Generators to the Part Level

KaiNinja extends TRELLIS.2 native 3D generation to part-level assets via a dual-volume O-Voxel representation, cutting whole-object Chamfer distance by 40%.

KaiNinja extends the TRELLIS.2 native 3D generator to produce part-level assets instead of one fused mesh, enabling downstream editing, rigging, and simulation. A dual-volume form of the O-Voxel representation solves the problem that a single volume cannot represent interfaces where two parts touch. The model needs no segmentation network, is partly trained on LLM-agent-authored part data, lowers whole-object Chamfer distance by 40%, and raises strict part F-score by 16% versus other part-generation pipelines.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research

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CoVeR: Coverage-Based Token Pruning for Multi-View 3D Reasoning in VLMs

CoVeR, a training-free coverage-based token pruner, preserves 93.5% of VLM 3D-reasoning performance using only about 8% of visual tokens.

Researchers introduce CoVeR, a deterministic, training-free selector that chooses visual tokens to cover every region of a multi-view 3D scene using only token coordinates. Unlike learned-importance and voxelization pruners, it enforces an exact per-scene token budget, avoids saturation plateaus, and prevents near-duplicate selections. Experiments across four vision-language models show it surpasses prior state of the art by 3.9 percentage points on average across three 3D reasoning benchmarks.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

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VoT: Vision-of-Thought for Unified Multimodal Representation Alignment

Researchers propose Vision-of-Thought (VoT), a discrete visual-planning token layer between VLMs and diffusion transformers improving text-to-image semantic alignment.

VoT introduces a discrete visual-thinking layer between vision-language models and diffusion transformers, letting the VLM act as a multimodal planner that emits tokens describing objects and layouts before pixel generation. A specialized VoT tokenizer is trained with VLM alignment, feature reconstruction, and vector-quantization losses. Experiments show improved semantic alignment and a structured, interpretable interface for controllable generation.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:53 UTC · 8d agoAI research

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Unified CT and MRI Pancreas Segmentation for Label-Efficient Cross-Modality Subregion Transfer

Domain-adversarial nnU-Net trained on 4,604 CT/MRI scans achieves 87.31% Dice pancreas segmentation with label-efficient subregion transfer.

A unified 3D pancreas segmentation framework applies domain-adversarial learning to 4,604 heterogeneous CT and MRI scans, aligning CT-MRI features via a latent domain discriminator on a shared nnU-Net encoder-decoder. Whole-pancreas segmentation reaches 87.31% Dice in-distribution and 84.20%-88.09% across external OOD datasets. The transferred encoder achieves 80.53% Dice on MRI and 83.05% on CT for downstream head-body-tail subregion segmentation using only limited MRI subregion annotations.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:40 UTC · 4d agoAI research

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Learning 3D Editing without Paired Supervision via Generative Prior Distillation

New framework distills 2D editing and VLM priors into a feed-forward 3D editing model without paired 3D training data.

The method, PriorEdit3D, learns feed-forward instruction-guided 3D editing by distilling knowledge from foundation models instead of using ground-truth 3D pairs. Through a differentiable rendering pipeline it supervises a 2D visual prior from an image editing model at the main view and a Vision-Language Model semantic prior at novel views for instruction fidelity and identity preservation. A 3D-aware Distribution Matching regularization constrains outputs to the manifold of realistic 3D assets defined by a pretrained image-to-3D teacher. Experiments report superior instruction fidelity and cross-view consistency over state-of-the-art baselines, with code released on GitHub.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

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RenderFormer-V2: Neural Rendering with Heterogeneous Scene Primitives

RenderFormer-V2 is a transformer-based neural renderer handling caustics, volumetric scattering and out-of-distribution materials without per-scene training or specialized code.

RenderFormer-V2 is a learned transformer-based neural rendering model that models global light transport as a sequence-to-sequence transformation, handling caustics, volumetric scattering, environment lighting, textured and displaced surfaces and out-of-distribution materials. It uses a two-stage process: a view-independent stage resolving primitive-to-primitive transport, and a view-dependent stage converting the neural scene representation into pixels. Improvements include combined windowed-attention with a rendering-informed attention sink for scalability, support for heterogeneous primitives like environment maps and participating media, and a surface-reflectance-independent neural material encoding, validated across diverse scenes with extensive ablations.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

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Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection

UCF-Net fuses CLIP and DINO features with entropy-based uncertainty weighting to improve generalizable deepfake image detection across generators.

Researchers propose UCF-Net, an uncertainty-aware cascaded fusion network that combines CLIP's language-aligned semantic priors with DINO's self-supervised visual-structure priors for deepfake detection. It aggregates hierarchical features across transformer depths via layer-wise expert modules and performs weighted fusion driven by entropy-derived uncertainty. The authors consolidate public deepfake datasets into a unified benchmark of roughly 4 million images plus a cross-generator set of over 8,000 faces from eight recent generators, where UCF-Net achieves the best mean AUC among evaluated methods, though zero-shot transfer remains challenging.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:42 UTC · 18h agoAI research

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VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification

VDiff-Bench, a 1,756-question benchmark, shows multimodal LLMs struggle with fine-grained image-difference identification, scoring as low as 8.7% on low-level changes.

VDiff-Bench is a multiple-choice benchmark of 1,756 four-way questions over image pairs covering 10 change categories including position, motion, color, texture, OCR/text and illumination, with curated hard negatives. Evaluation of 11 state-of-the-art open- and closed-source MLLMs shows fine-grained visual comparison remains brittle: 7-8B-scale open-source models score 52.5-70.6% on semantic changes but only 8.7-33.3% on low-level changes like noise and texture. Notably, Grok 4.3 shows a sharp performance drop on noise and texture differences, falling behind large open-source models like Kimi K2.5 and K3.

Hugging Face daily papers·Sep 4, 20:00 UTC · 11d agoAI research

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Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning

FactoSR factorizes 4D spatial reasoning into XY, Z, and T reinforcement-learning sub-objectives, boosting VLM performance on VSI-Bench by 5.9% and All-Angles-Bench by 4.5%.

Researchers present FactoSR, a factorized reinforcement learning framework that decomposes world-consistent reasoning into planar correspondence, depth consistency, and temporal reversibility sub-objectives. Optimizing these verifiable constraints turns the ill-posed projection recovery problem into tangible reasoning steps. Evaluations show gains of 5.9% on VSI-Bench and 4.5% on All-Angles-Bench for 3D and 4D reasoning, arguing VLMs' spatial bottleneck stems from training on 2D projections versus latent 3D geometry and temporal continuity.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

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SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem

Researchers release SpatialBlock-15k, a synthetic block-stacking dataset that improves 3D spatial reasoning in large vision-language models without dense geometric annotations.

The paper addresses limited spatial intelligence in LVLMs by training on structured block-manipulation tasks instead of costly real-scene annotated datasets. SpatialBlock-15k contains 15,000 synthetic problems covering 3D-to-2D projection, viewpoint transformation, and structural combination, with color modulation as visual cues. LVLMs trained on it via direct answering or reasoning-based prediction outperform baselines and generalize to real-world spatial tasks. Code and data are released on GitHub.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

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Can Edge-Deployable Vision-Language Models Identify Species?

Evaluation of 2-8B VLMs (Qwen3-VL, Gemma3) against BioCLIP on camera-trap species ID shows all models degrade sharply on field imagery.

The study tests whether edge-deployable 2-8B vision-language models carry genuine taxonomic knowledge, comparing Qwen3-VL 2B/4B/8B and Gemma3 4B against the 300M specialist BioCLIP on a 96-species task across clean iNaturalist photos and six LILA.science camera-trap collections. All models degrade 9.6-26.6 percentage points on field imagery, and BioCLIP outperforms every VLM by 33.2-59.2 points on an expanded 200-image sample, suggesting specialized data rather than scale drives the gap. Under open-set prompting, 5.9-9.6% of responses are syntactically valid but taxonomically nonexistent species names, with fabrication rankings replicating across evaluation sets.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 5d agoAI research1

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World in World: Explore the World with World Models

World in World is a training-free inference-time interface enabling camera-controlled rerendering, long-horizon revisiting, and motion transfer with frozen causal video world models.

The framework converts heterogeneous control evidence into camera- and time-labelled clean visual states that are read through the native self-attention of a frozen causal video model. Evidence includes source-video observations, target-view scene projections, geometry renderings for completing newly exposed regions, and retrieved generated states beyond the rolling cache. A correspondence router combines persistent point identities with geometry for token correspondences, while evidence-wise attention CFG independently regulates each auxiliary channel. The shared interface supports camera-controlled rerendering, long-horizon revisiting, and human-motion transfer without additional training.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

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To See a World in a Living Context: Unified Indoor-Outdoor Urban World Generation

Researchers introduce HoloWorld, a unified text-driven framework generating coherent indoor-outdoor 3D urban worlds, improving average AQS over SOTA by 7.68%.

HoloWorld is a text-driven 3D generation framework that unifies indoor and outdoor urban world generation using a continuously updated cross-scale world context. It autoregressively generates urban exteriors with consistent spatial organization, grounded in 3D building instances and footprints, then produces building-specific interiors with geometry-constrained layouts that inherit exterior appearance. The authors claim it is the first framework to unify indoor and outdoor generation within one coherent 3D urban world, reporting a 7.68% average AQS improvement over prior SOTA and the highest average RDR score.

Hugging Face daily papers·Aug 28, 20:00 UTC · 18d agoAI research

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SenseNova-U1.5: Towards Native Unified Visual Intelligence

SenseTime releases SenseNova-U1.5, an 8B-MoT encoder-free multimodal model unifying visual understanding, reasoning, and generation with native 4K resolution.

SenseNova-U1.5 is an 8B mixture-of-transformers multimodal model with an encoder-free, VAE-free architecture that understands, reasons about, and generates visual content at native resolutions up to 4K. Post-training optimizes specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing, consolidated through multi-expert on-policy distillation. Evaluations report gains in image fidelity, text rendering, multi-reference editing, and instruction following. The team plans to open-source training code including supervised fine-tuning, reinforcement learning, and on-policy distillation.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoModel release

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Rethinking Heterogeneous System Disaggregation for Subquadratic Attention

SQD disaggregates LLM inference by quadratic versus subquadratic attention layers, improving energy efficiency up to 56% on heterogeneous systems versus GPU-only baselines.

SQD (SubQuadratic Disaggregation) splits decode not by operator type but by quadratic versus subquadratic attention, matching their distinct arithmetic intensity and memory footprints. For sparse attention LLMs it separates top-k selection (requiring full KV indexing) from top-k attention plus FFN; for linear and sliding-window models it separates dense attention layers from subquadratic layers plus FFN. On an adjusted 8xB200 heterogeneous proxy, tokens-per-joule improves 53% on GLM 5.2, 31% on Nemotron 3 Ultra, and 56% on Gemma 4 31B. A Rubin plus LPX analytical model shows 1.2x-1.5x tighter achievable latencies and up to 3.6x higher throughput versus attention-FFN disaggregation.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:55 UTC · 4d agoAI research

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TempCloze: Can Video-LLMs Identify the Missing Middle?

TempCloze benchmark tests Video-LLMs' temporal reasoning with 1,521 videos, finding temporal alignment is the primary failure mode across 31 models.

TempCloze is a video cloze benchmark in which models must identify the true missing middle clip given the beginning and ending clips, using 1,521 carefully filtered videos from seven sources, mostly long-take and egocentric footage. Distractors are constructed along three dimensions: Semantic, Alignment and Progression, with shared scenes and objects to reduce appearance cues. Evaluation of 10 proprietary and 21 open-source Video-LLMs found Alignment is the primary bottleneck, with models often recognizing plausible semantics and local event progression but struggling with temporal alignment.

Hugging Face daily papers·Aug 31, 20:00 UTC · 15d agoAI research

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