[2606.23688] Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2606.23688 (cs)

[Submitted on 22 Jun 2026]

Title:Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

Authors:Yehonathan Litman, Xiaoxuan Ma, Manan Shah, Nicolas Ugrinovic, Kris Kitani, Fernando De la Torre, Shubham Tulsiani<br>View a PDF of the paper titled Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild, by Yehonathan Litman and 6 other authors

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Abstract:Reconstructing dynamic non-rigid objects from monocular video requires integrating visual cues from direct observations with data-driven priors over geometry and appearance. Prior approaches either learn to directly predict 4D representations from visual input or initialize a 3D representation that is subsequently deformed and refined based on video evidence. However, the former are constrained by the scarcity of 4D training data, while the latter leverage priors only for the initial reconstruction and rely solely on video supervision thereafter; neither handles complex in-the-wild scenarios with large deformations and occlusions well. We present Lift4D, a test-time optimization framework that addresses both limitations. First, we adapt an existing single-view 3D reconstruction model to yield temporally consistent per-frame predictions via causal latent conditioning, providing a coherent initialization for a deformable 3D Gaussian Splatting representation. We then ``sculpt'' this representation to match the input video through an occlusion-aware optimization that faithfully recovers visible surface details while completing unobserved regions using a view-conditioned diffusion prior. We demonstrate that Lift4D clearly improves over prior 4D reconstruction methods, particularly on challenging in-the-wild sequences with severe occlusions and non-rigid motion.

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Computer Vision and Pattern Recognition (cs.CV)

Cite as:<br>arXiv:2606.23688 [cs.CV]

(or<br>arXiv:2606.23688v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.23688

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Yehonathan Litman [view email]<br>[v1]<br>Mon, 22 Jun 2026 17:59:54 UTC (7,098 KB)

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