UGG unifies video generation and 4D geometric prediction
One model treats RGB, depth, and camera pose as conditional completion and can search scene hypotheses at inference.
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One model treats RGB, depth, and camera pose as conditional completion and can search scene hypotheses at inference.
Condition shifting estimates the generated velocity inside the generator and cuts peak GPU memory 4.4% and iteration time 9.2% versus DMD.
Authors say the method beats supervised fine-tuning and reinforcement learning with 3,000 samples while keeping general video quality.
Authors report a 3.35x KV-memory cut and 1.22x speedup on Causal Forcing at compression ratio 0.7.
The open-source stack also lists 74 benchmarks and 62 metrics, separating execution evidence from scores.

The video model is generally available on the Gemini API at $0.07 per second of 720p, 30% below Omni 1.1.
Video-Coder achieves 7-point gains on PhyGenBench and 24-point gains on VBench-2.0 via agentic reinforcement learning
The 13.7B–13.9B model makes 5.2–15 second video with stereo audio in 4–8 steps, under a restricted MiniMax license.
EmoReward distills reliability from training traces to adaptively allocate weights for facial motion and chromatic cues in video generation.
Kling-MLLM unifies image video audio inputs; captioning and prompt enhancement act as semantic contracts that maximize description density in the generation chain.
History Forcing is a distillation framework that strengthens historical supervision for few-step autoregressive video generators, achieving state-of-the-art results.
Wave Forcing co-designs generation training and execution for continuous video output achieving 1F1C with 147.6 DiT FPS on eight H200 GPUs.
Local inference outputs native 480p or 768p with stereo audio; the hosted API adds up to 4K.
Agent harness coordinates video models for agentic generation, world modeling, and recursive self-improvement in multi-shot video production.
Wizard uses skeleton and geometry proxies with adaptive masking to allow zero-shot control of characters in novel scenes via an agentic framework.
Trained on more than 130,000 examples, the 9B policy matches Claude Opus 5 while using nearly 33 times fewer tokens.
Across 2,151 rollouts, the strongest of nine systems meets every core requirement on 76.3% of 239 tasks.
The 8B diffusion model uses action-aware causal attention and a prompt enhancer; authors release weights, code, and benchmarks.
Authors report a five-second clip falling from 87.26 seconds to 22.45 seconds on one B200 with the fast decoder.
On VMem-Bench, retrieval lifted entity recall from 0.559 to 0.628-0.693 while precision and forbidden-entity avoidance fell.
Training covers 12 camera motions and 88 trajectories, including full 360-degree orbits, with surface-normal conditioning from reference views.
The adapter, H3-VBVR-I2V.safetensors, is trained on rebuilt VBVR data and loads in a MiniMax-H3 ComfyUI workflow.
CLACT adds multi-view character data, and GAMA injects geometrically aligned features to limit identity drift.
A 5B model matches 22B EchoWM navigation at 81.5 versus 81.6 and switches views 93.8% of the time.
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