Hong Chang

AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

Yiheng Li, Zhuo Li, Ruibing Hou +4

Conditional human motion generation remains a fundamental challenge in computer vision and robotics. Despite significant progress, current methods are often constrained by fixed mo…

LensWalk: Agentic Video Understanding by Planning How You See in Videos

Keliang Li, Yansong Li, Hongze Shen +3

The dense, temporal nature of video presents a profound challenge for automated analysis. Despite the use of powerful Vision-Language Models, prevailing methods for video understan…

DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning

Mingshuang Luo, Shuang Liang, Zhengkun Rong +7

Character image animation aims to synthesize high-fidelity videos by transferring motion from a driving sequence to a static reference image. Despite recent advancements, existing…

CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks

Mingshuang Luo, Ruibing Hou, Bo Chao +4

Human-centric visual analysis plays a pivotal role in diverse applications, including surveillance, healthcare, and human-computer interaction. With the emergence of large-scale un…

Revisiting Multimodal Positional Encoding in Vision-Language Models

Jie Huang, Xuejing Liu, Sibo Song +4

Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensi…

MotionVerse: A Unified Multimodal Framework for Motion Comprehension, Generation and Editing

Ruibing Hou, Mingshuang Luo, Hongyu Pan +2

This paper proposes MotionVerse, a unified framework that harnesses the capabilities of Large Language Models (LLMs) to comprehend, generate, and edit human motion in both single-p…

HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes

Keliang Li, Hongze Shen, Hao Shi +9

The aspiration for artificial general intelligence, fueled by the rapid progress of multimodal models, demands human-comparable performance across diverse environments. We propose…

un $^{2}$ CLIP: Improving CLIP's Visual Detail Capturing Ability via Inverting unCLIP

Yinqi Li, Jiahe Zhao, Hong Chang +3

Contrastive Language-Image Pre-training (CLIP) has become a foundation model and has been applied to various vision and multimodal tasks. However, recent works indicate that CLIP f…

DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks

Yinqi Li, Hong Chang, Ruibing Hou +2

Diffusion models have shown remarkable progress in various generative tasks such as image and video generation. This paper studies the problem of leveraging pretrained diffusion mo…

HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding

Jiahe Zhao, Ruibing Hou, Zejie Tian +2

We propose a new task to benchmark human-in-scene understanding for embodied agents: Human-In-Scene Question Answering (HIS-QA). Given a human motion within a 3D scene, HIS-QA requ…

RefHCM: A Unified Model for Referring Perceptions in Human-Centric Scenarios

Jie Huang, Ruibing Hou, Jiahe Zhao +2

Human-centric perceptions play a crucial role in real-world applications. While recent human-centric works have achieved impressive progress, these efforts are often constrained to…

UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing

Yiheng Li, Ruibing Hou, Hong Chang +2

Human pose plays a crucial role in the digital age. While recent works have achieved impressive progress in understanding and generating human poses, they often support only a sing…

Morph: A Motion-free Physics Optimization Framework for Human Motion Generation

Zhuo Li, Mingshuang Luo, Ruibing Hou +5

Human motion generation has been widely studied due to its crucial role in areas such as digital humans and humanoid robot control. However, many current motion generation approach…