12 citations · 35 across the 21 of their papers we have counts for
25 papers
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…
LEMUR: Latent Entropy-aware Multimodal Unlearning via Visual-anchored Reasoning Redirection
Xinhao Zhong, Yuxia Qiao, Junhao Li +3
Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially improving visual reasoning. Howev…
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…
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…
C2P-VAR: Continual and Compositional Personalization in Visual Autoregressive Models
Junhao Li, Xinhao Zhong, Yi sun +4
Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation, yet their personalization capabilities remain largely limited to sta…
VDOT: Efficient Unified Video Creation via Optimal Transport Distillation
Yutong Wang, Haiyu Zhang, Tianfan Xue +4
The rapid development of generative models has significantly advanced image and video applications. Among these, video creation, aimed at generating videos under various conditions…
ShotDirector: Directorially Controllable Multi-Shot Video Generation with Cinematographic Transitions
Xiaoxue Wu, Xinyuan Chen, Yaohui Wang +1
Shot transitions play a pivotal role in multi-shot video generation, as they determine the overall narrative expression and the directorial design of visual storytelling. However,…
RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling
Bingjie Gao, Qianli Ma, Xiaoxue Wu +9
Prompt design plays a crucial role in text-to-video (T2V) generation, yet user-provided prompts are often short, unstructured, and misaligned with training data, limiting the gener…
LIA-X: Interpretable Latent Portrait Animator
Yaohui Wang, Di Yang, Xinyuan Chen +3
We introduce LIA-X, a novel interpretable portrait animator designed to transfer facial dynamics from a driving video to a source portrait with fine-grained control. LIA-X is an au…
CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models
Xiaoxue Wu, Bingjie Gao, Yu Qiao +2
Despite significant advances in video synthesis, research into multi-shot video generation remains in its infancy. Even with scaled-up models and massive datasets, the shot transit…
The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation
Bingjie Gao, Xinyu Gao, Xiaoxue Wu +5
The evolution of Text-to-video (T2V) generative models, trained on large-scale datasets, has been marked by significant progress. However, the sensitivity of T2V generative models…
AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset
Haiyu Zhang, Xinyuan Chen, Yaohui Wang +3
Diffusion models have achieved remarkable progress in the field of video generation. However, their iterative denoising nature requires a large number of inference steps to generat…
Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
Xin Ma, Yaohui Wang, Gengyun Jia +4
Diffusion models have achieved great progress in image animation due to powerful generative capabilities. However, maintaining spatio-temporal consistency with detailed information…
Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion
Hao Wen, Zehuan Huang, Yaohui Wang +2
Existing single image-to-3D creation methods typically involve a two-stage process, first generating multi-view images, and then using these images for 3D reconstruction. However,…
4Diffusion: Multi-view Video Diffusion Model for 4D Generation
Haiyu Zhang, Xinyuan Chen, Yaohui Wang +3
Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. However, these methods lack multi-view spatial-temporal modeli…
Latte: Latent Diffusion Transformer for Video Generation
Xin Ma, Yaohui Wang, Xinyuan Chen +5
We propose Latte, a novel Latent Diffusion Transformer for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer b…
Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model
Lingjun Zhang, Xinyuan Chen, Yaohui Wang +2
Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating m…
EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion
Zehuan Huang, Hao Wen, Junting Dong +8
Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation i…
VideoBooth: Diffusion-based Video Generation with Image Prompts
Yuming Jiang, Tianxing Wu, Shuai Yang +5
Text-driven video generation witnesses rapid progress. However, merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users'…
VBench: Comprehensive Benchmark Suite for Video Generative Models
Ziqi Huang, Yinan He, Jiashuo Yu +13
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable…
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Yufei Wang, Wenhan Yang, Xinyuan Chen +7
While super-resolution (SR) methods based on diffusion models exhibit promising results, their practical application is hindered by the substantial number of required inference ste…
SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction
Xinyuan Chen, Yaohui Wang, Lingjun Zhang +7
Recently video generation has achieved substantial progress with realistic results. Nevertheless, existing AI-generated videos are usually very short clips ("shot-level") depicting…