8 citations · 11 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
Yixuan Han, Fan Ma, Ruijie Quan +1
Test-Time Scaling (TTS) enhances the reasoning ability of large language models (LLMs) by allocating additional computation during inference. However, existing approaches primarily…
Adversarial-Guided Diffusion for Multimodal LLM Attacks
Chengwei Xia, Fan Ma, Ruijie Quan +2
This paper addresses the challenge of generating adversarial image using a diffusion model to deceive multimodal large language models (MLLMs) into generating the targeted response…
Insert Anything: Image Insertion via In-Context Editing in DiT
Wensong Song, Hong Jiang, Zongxing Yang +2
This work presents Insert Anything, a unified framework for reference-based image insertion that seamlessly integrates objects from reference images into target scenes under flexib…
DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization
Zhenglin Zhou, Xiaobo Xia, Fan Ma +3
Text-to-3D generation automates 3D content creation from textual descriptions, which offers transformative potential across various fields. However, existing methods often struggle…
BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities
Zhibo Tian, Ruijie Quan, Fan Ma +2
Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused…