4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere
We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple motion from geometry or produce limited 4D attributes such as sparse trajectories or two-view scene flow, 4RC learns a holistic 4D representation that jointly captures dense scene geometry and motion dynamics. At its core, 4RC introduces a novel encode-once, query-anywhere and anytime paradigm: a transformer backbone encodes the entire video into a compact spatio-temporal latent space, from which a conditional decoder can efficiently query 3D geometry and motion for any query frame at any target timestamp. To facilitate learning, we represent per-view 4D attributes in a minimally factorized form by decomposing them into base geometry and time-dependent relative motion. Extensive experiments demonstrate that 4RC outperforms prior and concurrent methods across a wide range of 4D reconstruction tasks.
699c6c42cf1450b05134df18
Yihang Luo
claimed_verified
2026-02-24T09:51:27.544Z
654b607b9296401f072e7cf5
https://cdn-avatars.huggingface.co/v1/production/uploads/654b607b9296401f072e7cf5/5ys396eFk9x_vKt5Aot8G.jpeg
Yihang Luo
Luo-Yihang
user
Luo-Yihang
699c6c42cf1450b05134df19
Shangchen Zhou
699c6c42cf1450b05134df1a
Yushi Lan
699c6c42cf1450b05134df1b
Xingang Pan
699c6c42cf1450b05134df1c
Chen Change Loy
4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere
We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple motion from geometry or produce limited 4D attributes such as sparse trajectories or two-view scene flow, 4RC learns a holistic 4D representation that jointly captures dense scene geometry and motion dynamics. At its core, 4RC introduces a novel encode-once, query-anywhere and anytime paradigm: a transformer backbone encodes the entire video into a compact spatio-temporal latent space, from which a conditional decoder can efficiently query 3D geometry and motion for any query frame at any target timestamp. To facilitate learning, we represent per-view 4D attributes in a minimally factorized form by decomposing them into base geometry and time-dependent relative motion. Extensive experiments demonstrate that 4RC outperforms prior and concurrent methods across a wide range of 4D reconstruction tasks.
4RC presents a unified feed-forward framework for 4D reconstruction from monocular videos that learns holistic scene geometry and motion dynamics through a transformer-based encoder-decoder architecture with conditional querying capabilities.
4D reconstruction monocular videos feed-forward framework spatio-temporal latent space transformer backbone conditional decoder 3D geometry motion dynamics encode-once query-anywhere paradigm