1 citations · 1 across the 11 of their papers we have counts for
6 papers · 1 filter
Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies
Wenjin Hou, Wei Liu, Han Hu +3
Multimodal Large Language Models (MLLMs) have shown remarkable proficiency on general-purpose vision-language benchmarks, reaching or even exceeding human-level performance. Howeve…
ITS3D: Inference-Time Scaling for Text-Guided 3D Diffusion Models
Zhenglin Zhou, Fan Ma, Xiaobo Xia +3
We explore inference-time scaling in text-guided 3D diffusion models to enhance generative quality without additional training. To this end, we introduce ITS3D, a framework that fo…
AnchorFlow: Training-Free 3D Editing via Latent Anchor-Aligned Flows
Zhenglin Zhou, Fan Ma, Chengzhuo Gui +4
Training-free 3D editing aims to modify 3D shapes based on human instructions without model finetuning. It plays a crucial role in 3D content creation. However, existing approaches…
Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video Diffusion
Zhenglin Zhou, Fan Ma, Hehe Fan +1
Animatable head avatar generation typically requires extensive data for training. To reduce the data requirements, a natural solution is to leverage existing data-free static avata…
TV-Dialogue: Crafting Theme-Aware Video Dialogues with Immersive Interaction
Sai Wang, Fan Ma, Xinyi Li +2
Recent advancements in LLMs have accelerated the development of dialogue generation across text and images, yet video-based dialogue generation remains underexplored and presents u…
InfiniDreamer: Arbitrarily Long Human Motion Generation via Segment Score Distillation
Wenjie Zhuo, Fan Ma, Hehe Fan
We present InfiniDreamer, a novel framework for arbitrarily long human motion generation. InfiniDreamer addresses the limitations of current motion generation methods, which are ty…