3 citations · 6 across the 22 of their papers we have counts for
23 papers
Generator-Aligned Reward Modeling with Target-Aware Bounded Optimization for Visual Generation
Ziqiao Weng, Yuanzhi Liang, Haibing Huang +2
Post-training aims to enhance desired attributes in visual generative models while preserving their general generation capabilities. Reward-guided post-training faces two challenge…
Dreaming in Flow: Bidirectional Self-Refinement for Unified Multimodal Models
Ke Hao, Yuanzhi Liang, Tingxi Chen +5
Unified multimodal models can generate images and interpret them, but their two capabilities are usually optimized with separate supervision. We introduce Bidirectional Self-Refine…
Sample-Adaptive Latent Rewards for Uncertainty-Guided Diffusion Post-Training
Rui Li, Yuanzhi Liang, Ke Hao +4
Latent reward models can supervise visual diffusion models without decoding intermediate states into pixel space. This makes alignment with human preferences more efficient. Howeve…
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
Yuanzhi Liang, Xufeng Zhan, Haibin Huang +2
Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have adva…
Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation
Rui Li, Ke Hao, Yuanzhi Liang +4
Reinforcement learning, particularly Group Relative Policy Optimization (GRPO), has emerged as an effective framework for post-training visual generative models with human preferen…
Reward-Aware Trajectory Shaping for Few-step Visual Generation
Rui Li, Bingyu Li, Yuanzhi Liang +3
Achieving high-fidelity generation in extremely few sampling steps has long been a central goal of generative modeling. Existing approaches largely rely on distillation-based frame…
Tele-Omni: a Unified Multimodal Framework for Video Generation and Editing
Jialun Liu, Tian Li, Xiao Cao +20
Recent advances in diffusion-based video generation have substantially improved visual fidelity and temporal coherence. However, most existing approaches remain task-specific and r…
TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generation
Yuanzhi Liang, Xuan'er Wu, Yirui Liu +12
Post-training is the decisive step for converting a pretrained video generator into a production-oriented model that is instruction-following, controllable, and robust over long te…
TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model
Yabo Chen, Yuanzhi Liang, Jiepeng Wang +24
World models aim to endow AI systems with the ability to represent, generate, and interact with dynamic environments in a coherent and temporally consistent manner. While recent vi…
CtrlVDiff: Controllable Video Generation via Unified Multimodal Video Diffusion
Dianbing Xi, Jiepeng Wang, Yuanzhi Liang +8
We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g.…
Learning What to Trust: Bayesian Prior-Guided Optimization for Visual Generation
Ruiying Liu, Yuanzhi Liang, Haibin Huang +2
Group Relative Policy Optimization (GRPO) has emerged as an effective and lightweight framework for post-training visual generative models. However, its performance is fundamentall…
UniModel: A Visual-Only Framework for Unified Multimodal Understanding and Generation
Chi Zhang, Jiepeng Wang, Youming Wang +5
We present UniModel, a unified generative model that jointly supports visual understanding and visual generation within a single pixel-to-pixel diffusion framework. Our goal is to…
SpatialLock: Precise Spatial Control in Text-to-Image Synthesis
Biao Liu, Yuanzhi Liang
Text-to-Image (T2I) synthesis has made significant advancements in recent years, driving applications such as generating datasets automatically. However, precise control over objec…
Rethinking Reward Signals in Video GRPO: When Scores Become Targets
Rui Li, Yuanzhi Liang, Ziqi Ni +3
Group Relative Policy Optimization (GRPO) enables stable and preference-oriented updates via group-wise comparisons for post-training video generation. However, GRPO directly optim…
Seeing What Matters: Visual Preference Policy Optimization for Visual Generation
Ziqi Ni, Yuanzhi Liang, Rui Li +4
Reinforcement learning (RL) has become a powerful tool for post-training visual generative models, with Group Relative Policy Optimization (GRPO) increasingly used to align generat…
Uni-Inter: Unifying 3D Human Motion Synthesis Across Diverse Interaction Contexts
Sheng Liu, Yuanzhi Liang, Jiepeng Wang +3
We present Uni-Inter, a unified framework for human motion generation that supports a wide range of interaction scenarios: including human-human, human-object, and human-scene-with…
LaxMotion: Rethinking Supervision Granularity for 3D Human Motion Generation
Sheng Liu, Yuanzhi Liang, Sidan Du
Recent 3D human motion generation models demonstrate remarkable reconstruction accuracy yet struggle to generalize beyond training distributions. This limitation arises partly from…
InterSyn: Interleaved Learning for Dynamic Motion Synthesis in the Wild
Yiyi Ma, Yuanzhi Liang, Xiu Li +2
We present Interleaved Learning for Motion Synthesis (InterSyn), a novel framework that targets the generation of realistic interaction motions by learning from integrated motions…
Integrating Reinforcement Learning with Visual Generative Models: Foundations and Advances
Yuanzhi Liang, Yijie Fang, Ke Hao +4
Generative models have made significant progress in synthesizing visual content, including images, videos, and 3D/4D structures. However, they are typically trained with surrogate…
AI Flow: Perspectives, Scenarios, and Approaches
Hongjun An, Wenhan Hu, Sida Huang +11
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and c…
OmniVDiff: Omni Controllable Video Diffusion for Generation and Understanding
Dianbing Xi, Jiepeng Wang, Yuanzhi Liang +5
In this paper, we propose a novel framework for controllable video diffusion, OmniVDiff , aiming to synthesize and comprehend multiple video visual content in a single diffusion mo…
TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
Yangguang Li, Zi-Xin Zou, Zexiang Liu +8
Recent advancements in diffusion techniques have propelled image and video generation to unprecedented levels of quality, significantly accelerating the deployment and application…
VAST 1.0: A Unified Framework for Controllable and Consistent Video Generation
Chi Zhang, Yuanzhi Liang, Xi Qiu +2
Generating high-quality videos from textual descriptions poses challenges in maintaining temporal coherence and control over subject motion. We propose VAST (Video As Storyboard fr…