Yaoqin Ye

Xia Qi, Peishan Cong, Yichen Yao et al.

Video object insertion is a critical task for dynamically inserting new objects into existing environments. Previous video generation methods focus primarily on synthesizing entire scenes while struggling with ensuring consistent object appearance, spatial alignment, and temporal coherence when inserting objects into existing videos. In this paper, we propose a novel solution for Video Object Insertion, which integrates multi-view object priors to address the common challenges of appearance inconsistency and occlusion handling in dynamic environments. By lifting 2D reference images into multi-view representations and leveraging a dual-path view-consistent conditioning mechanism, our framework ensures stable identity guidance and robust integration across diverse viewpoints. A quality-aware weighting mechanism is also employed to adaptively handle noisy or imperfect inputs. Additionally, we introduce an Integration-Aware Consistency Module that guarantees spatial realism, effectively resolving occlusion and boundary artifacts while maintaining temporal continuity across frames. Experimental results show that our solution significantly improves the quality of video object insertion, providing stable and realistic integration.

Yaoqin Ye, Junjie Zhang, Hongwei Shi

The task of medical image recognition is notably complicated by the presence of varied and multiple pathological indications, presenting a unique challenge in multi-label classification with unseen labels. This complexity underlines the need for computer-aided diagnosis methods employing multi-label zero-shot learning. Recent advancements in pre-trained vision-language models (VLMs) have showcased notable zero-shot classification abilities on medical images. However, these methods have limitations on leveraging extensive pre-trained knowledge from broader image datasets, and often depend on manual prompt construction by expert radiologists. By automating the process of prompt tuning, prompt learning techniques have emerged as an efficient way to adapt VLMs to downstream tasks. Yet, existing CoOp-based strategies fall short in performing class-specific prompts on unseen categories, limiting generalizability in fine-grained scenarios. To overcome these constraints, we introduce a novel prompt generation approach inspirited by text generation in natural language processing (NLP). Our method, named Pseudo-Prompt Generating (PsPG), capitalizes on the priori knowledge of multi-modal features. Featuring a RNN-based decoder, PsPG autoregressively generates class-tailored embedding vectors, i.e., pseudo-prompts. Comparative evaluations on various multi-label chest radiograph datasets affirm the superiority of our approach against leading medical vision-language and multi-label prompt learning methods. The source code is available at https://github.com/fallingnight/PsPG

Yaoqin Ye, Yiteng Xu, Qin Sun et al.

Human behaviors in real-world environments are inherently interactive, with an individual's motion shaped by surrounding agents and the scene. Such capabilities are essential for applications in virtual avatars, interactive animation, and human-robot collaboration. We target real-time human interaction-to-reaction generation, which generates the ego's future motion from dynamic multi-source cues, including others' actions, scene geometry, and optional high-level semantic inputs. This task is fundamentally challenging due to (i) limited and fragmented interaction data distributed across heterogeneous single-person, human-human, and human-scene domains, and (ii) the need to produce low-latency yet high-fidelity motion responses during continuous online interaction. To address these challenges, we propose ReMoGen (Reaction Motion Generation), a modular learning framework for real-time interaction-to-reaction generation. ReMoGen leverages a universal motion prior learned from large-scale single-person motion datasets and adapts it to target interaction domains through independently trained Meta-Interaction modules, enabling robust generalization under data-scarce and heterogeneous supervision. To support responsive online interaction, ReMoGen performs segment-level generation together with a lightweight Frame-wise Segment Refinement module that incorporates newly observed cues at the frame level, improving both responsiveness and temporal coherence without expensive full-sequence inference. Extensive experiments across human-human, human-scene, and mixed-modality interaction settings show that ReMoGen produces high-quality, coherent, and responsive reactions, while generalizing effectively across diverse interaction scenarios.