Few-step distillation improves the efficiency of autoregressive (AR) video generation, but often causes diversity collapse: under the same prompt, different noise samples tend to produce highly similar videos with weakened motion dynamics. We analyze this degradation in Distribution Matching Distillation (DMD)-distille...
Zi-Xuan Duan, Xun-Zhi Xiang, Yabo Chen et al.· 0 citations
We present DistScene, a framework for single-image compositional 3D scene generation by jointly modeling the environment and individual objects. Unlike existing methods that represent scenes primarily as collections of objects, we model the environment as an explicit scene component to provide geometric context for obj...
Kun-Ming Luo, Hong-Yu Yan, Ken Deng et al.· 0 citations
Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Generative Grounding Feedback(GGF), a self-evolving post-training framework that uses only text prompts and the model's own visual experience....
World Action Models (WAMs) augment robot action generation with future visual supervision. Existing WAMs commonly fuse main and wrist observations into one visual stream and train both with the same future-video objective, despite their different visual dynamics. A stable main camera reveals scene-level task evolution,...
Jie Wu, Yu-Zhi Huang, Jun-Qi Liu et al.· 0 citations
TourPhysics combines deterministic simulation with video generation while assigning separate roles to simulator state, geometric evidence, generator controls, and appearance memory, an online framework initialized from a single image and a declarative physical configuration.
Xin Zhang, Yabo Chen, Zi-Xuan Duan et al.· 2 citations
A unified conditional-flow framework that casts generation, semantic editing, and intra-structural retargeting as condition-modulated transport within one text- and skeleton-conditioned rectified-flow model, replacing separate pipelines with a single conditional motion transport model while keeping the same-topology re...
Junli Li, Xin Song, Si-Qi Wang et al.· arXiv.org· 1 citation
Experimental results show that GenTrans supports effective video transmission under ultra-low-bitrate and weak-network conditions, achieving improved transmission efficiency, decoding efficiency, and robustness while preserving perceptual quality.
Xiangyu Chen, Ji-Xiang Luo, Yuan-Kai Fan et al.· arXiv.org· 2 citations
This paper proposes a unified framework named CineWeaver to achieve reference-controllable multi-shot long-video generation without retraining, and is the first unified framework to simultaneously enable long-form, reference-controllable, and multi-shot video generation in a training-free fashion.
Yuyang Huang, Yabo Chen, Wen-Rui Dai et al.· arXiv.org· 4 citations
RefVideo-6M, a large-scale reference-guided editing dataset containing 5 million video editing samples and 1 million image editing samples, is introduced and enables the training of powerful editing models with improved visual quality, controllability, and reference consistency.
Bojia Zi, Xiaoyan Yang, Yu Zhou et al.· 0 citations
A unified latent-space framework for image and video diffusion models that achieves the sota performance among various metrics and further improves optimization stability and achieves the highest VBench quality, semantic, and total scores among the evaluated methods.
Rui Li, Yuan-Zhi Liang, Ke-Chun Hao et al.· 0 citations
This work proposes a co-evolution roadmap for physical intelligence centered on theembodied brain, a long-term model target for integrating multimodal context, comparing candidate interventions, and issuing state-transition or capability requests rather than direct actuator commands.
Yuanzhi Liang, Xufeng Zhan, Haibin Huang et al.· 1 citation
This work proposes "Directing the World", a fast autoregressive framework for controllable world-model video generation with compositional human-motion and camera-trajectory control, and introduces a Fast-Slow Memory training strategy to stabilize long-horizon rollout learning and improve convergence.
