4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation

Chiao-An Yang, Ryo Hachiuma, Sifei Liu, Subhashree Radhakrishnan, Raymond A. Yeh, Yu-Chiang Frank Wang, Min-Hung Chen

Keywords: 4D understanding, MLLM, VLM, Region-level understanding

TL;DR: We propose: 1) 4D-RGPT, a specialized MLLM designed to capture 4D representations from video inputs with enhanced temporal perception, and 2) R4D-Bench, a new benchmark for depth-aware dynamic scenes with region-level prompting.

Abstract: Despite advances in Multimodal LLMs (MLLMs), their ability to reason over 3D structures and temporal dynamics remains limited, constrained by weak 4D perception and temporal understanding. Existing 3D and 4D Video Question Answering (VQA) benchmarks also emphasize static scenes and lack region-level prompting. We tackle these issues by introducing: (a) 4D-RGPT, a specialized MLLM designed to capture 4D representations from video inputs with enhanced temporal perception; (b) Perceptual 4D Distillation (P4D), a training framework that transfers 4D representations from a frozen expert model into 4D-RGPT for comprehensive 4D perception; and (c) \ourbenchmark, a benchmark for depth-aware dynamic scenes with region-level prompting, built via a hybrid automated and human-verified pipeline. Our 4D-RGPT achieves notable improvements on both existing 4D VQA benchmarks and the proposed R4D-Bench benchmark. Project page: https://www.ca-joe-yang.com/resource/projects/4D_RGPT/.

Previously Accepted: Yes

Previous Venue: CVPR 2026

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Submission Number: 12