12 citations · 36 across the 32 of their papers we have counts for
13 papers · 2 filters
Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
Qian Yu, Zongyang Qiu, Juncheng Yan +40
Unified multimodal generation covers discrete, continuous, and hybrid paradigms. Discrete approaches with single-codebook visual tokenizers align naturally with text modeling but h…
CineMem: Attention-Aligned Fine-grained Memory Retrieval for Multi-Shot Long Video Generation
Xiaoxue Wu, Xinyu Gao, Fang Yu +2
Autoregressive video generation relies on memory retrieval to model long-range dependencies. This capability becomes particularly critical for multi-shot generation, where models m…
FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching
Ruchang Yao, Shi Guo, Junhao Zhuang +7
Recent video super-resolution (VSR) models achieve efficient one-step generation through diffusion distillation. However, high-resolution inference remains costly, and distilled mo…
VDOT++: Unified Few-Step Video Generation via Unbalanced Optimal Transport Distillation
Yutong Wang, Xingtong Ge, Enhuai Liu +6
Video creation spans text-to-video (T2V), image-to-video (I2V), and condition-based generation, yet video diffusion models remain costly because they repeatedly evaluate large back…
PulseQuant: Propagation-Guided Subspace Correction for 4-Bit Video Diffusion Transformers
Yutong Wang, Xingtong Ge, Enhuai Liu +4
Quantization errors in video diffusion transformers can be amplified or attenuated by subsequent denoising updates, making local reconstruction error an incomplete predictor of fin…
Diff-VF: Training-free High-quality Long Video Generation via Diffusion Model
Haoning Yang, Xinyuan Chen, Yaohui Wang +1
Recently, diffusion models have made great progress in video generation. However, most existing video diffusion models are trained with short videos, and degrade when extrapolated…
DeforM: Reasoning-Guided Physics-Aware Video Generation via Spatial-Temporal Masking
Yunyi Li, Yu Qiao, Yaohui Wang +1
Video generation models achieve high visual quality but often struggle to generate physics-aware videos. Unlike rigid-body motion, which can be described by explicit trajectories o…
Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation
Chang Liu, Mingwen Shao, Xiang Lv +5
Recent breakthroughs in 3D generation have advanced notably with the development of text-to-image diffusion model. However, existing methods remain two practical challenges: (1) Th…
Policy-as-Data: Learning Generalizable HOI Diffusion Models from Simulated Physics
Shujia Li, Jianshu Hu, Haiyu Zhang +5
Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current data-driven approaches primar…
CausalMotion: Structured Physical Reasoning as Keyframe and Trajectory Guidance for Training-Free Video Generation
Sihan Zhuang, Xinyuan Chen, Tianfan Xue +1
Recent advances in diffusion-based video generation have significantly improved visual quality and short-term temporal coherence. However, existing methods still struggle to produc…
PARE: Pruning and Adaptive Routing for Efficient Video Generation
Yutong Wang, Yunke Wang, Tianfan Xue +4
Video Diffusion Transformers (DiTs) generate high-quality videos but demand substantial compute due to wide blocks, deep architectures, and iterative sampling. Recent methods reduc…
BEAT: Rhythm-Elastic Alignment for Agentic Music-guided Movie Trailer Generation
Yutong Wang, Yunke Wang, Xinyuan Chen +1
Automatic movie trailer generation must select shots from a full-length film and synchronize them with background music. Existing methods either relegate music alignment to post-pr…
Uni-Animator: Towards Unified Visual Colorization
Xinyuan Chen, Yao Xu, Shaowen Wang +2
We propose Uni-Animator, a novel Diffusion Transformer (DiT)-based framework for unified image and video sketch colorization. Existing sketch colorization methods struggle to unify…