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Yiqian Chang

5 accepted papers

2026

BulletTime4D: Towards High Spatio-Temporal Resolution Dynamic Scene Rendering via Spike-Guided Stereo Vision

AAAI 2026technical

High spatio‑temporal resolution novel‑view scene rendering is crucial for applications such as sports analysis and scientific experiments. However, existing Dynamic Scene Rendering (DSR) approaches typically rely on conventional RGB cameras with limited frame rates, making it difficult to achieve hi

Cited by 0SourcePDFScholar
2026

MER-Tracker: Towards High-Speed 3D Point Tracking via Multi-View Event-RGB Hybrid Cameras

CVPR 2026

This paper proposes the first task for high-speed 3D point tracking using multi-view Event-RGB hybrid cameras. We design a cuboid observation device comprising 4 RGB cameras (30fps) and 2 Event cameras to synchronously capture high-speed motions, and propose MER-Tracker, a high-frame-rate 3D point-t

Cited by 0SourceScholar
2026

Resolving the Stability-Plasticity Dilemma in Reinforcement Learning via Complementary Continual Critics

CVPR 2026

This paper proposes the Continual Dual-Critic with Cross-Attention (CD-CCA) framework for visual reinforcement learning to address the plasticity-stability conflict. Our method introduces continual learning techniques into the visual RL architecture, constructing two complementary critics using Cont

Cited by 0SourcecodeScholar
2025

Spike4DGS: Towards High-Speed Dynamic Scene Rendering with 4D Gaussian Splatting via a Spike Camera Array

NeurIPS 2025poster

Spike camera with high temporal resolution offers a new perspective on high-speed dynamic scene rendering. Most existing rendering methods rely on Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) for static scenes using a monocular spike camera. However, these methods struggle with dyna…

Cited by 0SourcecodeScholar
2025

VLMs-Guided Representation Distillation for Efficient Vision-Based Reinforcement Learning

CVPR 2025poster

Vision-based Reinforcement Learning (VRL) attempts to establish associations between visual inputs and optimal actions through interactions with the environment. Given the high-dimensional and complex nature of visual data, it becomes essential to learn policy upon high-quality state representation.…

Cited by 0SourcePDFScholar