47 citations · 106 across the 24 of their papers we have counts for
10 papers · 1 filter
Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance
Ruihang Chu, Yefei He, Zhekai Chen +10
We present Wan-Move, a simple and scalable framework that brings motion control to video generative models. Existing motion-controllable methods typically suffer from coarse contro…
Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning
Xinyu Liu, Hangjie Yuan, Yujie Wei +9
Unified video models exhibit strong capabilities in understanding and generation, yet they struggle with reason-informed visual editing even when equipped with powerful internal vi…
Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance
Yujie Wei, Shiwei Zhang, Hangjie Yuan +8
Mixture-of-Experts (MoE) has emerged as a powerful paradigm for scaling model capacity while preserving computational efficiency. Despite its notable success in large language mode…
Exploiting Discriminative Codebook Prior for Autoregressive Image Generation
Longxiang Tang, Ruihang Chu, Xiang Wang +6
Advanced discrete token-based autoregressive image generation systems first tokenize images into sequences of token indices with a codebook, and then model these sequences in an au…
TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation
Zhekai Chen, Ruihang Chu, Yukang Chen +4
Scaling visual generation models is essential for real-world content creation, yet requires substantial training and computational expenses. Alternatively, test-time scaling has ga…
Turning Internal Gap into Self-Improvement: Promoting the Generation-Understanding Unification in MLLMs
Yujin Han, Hao Chen, Andi Han +5
Although unified MLLMs aim to unify generation and understanding, they are considered to exhibit an internal gap, with understanding outperforming generation. Through large-scale e…
Wan: Open and Advanced Large-Scale Video Generative Models
Team Wan, Ang Wang, Baole Ai +58
This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transfo…
UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
Xiang Wang, Shiwei Zhang, Longxiang Tang +4
This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animat…
Taming Consistency Distillation for Accelerated Human Image Animation
Xiang Wang, Shiwei Zhang, Hangjie Yuan +5
Recent advancements in human image animation have been propelled by video diffusion models, yet their reliance on numerous iterative denoising steps results in high inference costs…
DreamRelation: Relation-Centric Video Customization
Yujie Wei, Shiwei Zhang, Hangjie Yuan +8
Relational video customization refers to the creation of personalized videos that depict user-specified relations between two subjects, a crucial task for comprehending real-world…