10 papers
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…
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…
unCLIP: 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…