WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments
We present WildRayZer, a self-supervised framework for novel view synthesis (NVS) in dynamic environments where both the camera and objects move. Dynamic content breaks the multi-view consistency that static NVS models rely on, leading to ghosting, hallucinated geometry, and unstable pose estimation. WildRayZer addresses this by performing an analysis-by-synthesis test: a camera-only static renderer explains rigid structure, and its residuals reveal transient regions. From these residuals, we construct pseudo motion masks, distill a motion estimator, and use it to mask input tokens and gate loss gradients so supervision focuses on cross-view background completion. To enable large-scale training and evaluation, we curate Dynamic RealEstate10K (D-RE10K), a real-world dataset of 15K casually captured dynamic sequences, and D-RE10K-iPhone, a paired transient and clean benchmark for sparse-view transient-aware NVS. Experiments show that WildRayZer consistently outperforms optimization-based and feed-forward baselines in both transient-region removal and full-frame NVS quality with a single feed-forward pass.
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Xuweiyi Chen
admin_assigned
2026-01-16T15:45:11.912Z
634632aaac1cb29fb2ac9f14
https://cdn-avatars.huggingface.co/v1/production/uploads/634632aaac1cb29fb2ac9f14/nGZ2TzKOOcKMAR_NFYKkR.jpeg
Xuweiyi Chen
Xuweiyi
user
Xuweiyi
6969dd8432f0333869ff952a
Wentao Zhou
admin_assigned
2026-01-16T15:45:05.448Z
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Wentao Zhou
Chasers1
user
Chasers1
6969dd8432f0333869ff952b
Zezhou Cheng
admin_assigned
2026-01-16T15:44:58.848Z
64834da6c8b6f4a798f9f09d
/avatars/5f9dd5ce3acd27434eb511660bb7f430.svg
Cheng
Zezhou
user
Zezhou
WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments
We present WildRayZer, a self-supervised framework for novel view synthesis (NVS) in dynamic environments where both the camera and objects move. Dynamic content breaks the multi-view consistency that static NVS models rely on, leading to ghosting, hallucinated geometry, and unstable pose estimation. WildRayZer addresses this by performing an analysis-by-synthesis test: a camera-only static renderer explains rigid structure, and its residuals reveal transient regions. From these residuals, we construct pseudo motion masks, distill a motion estimator, and use it to mask input tokens and gate loss gradients so supervision focuses on cross-view background completion. To enable large-scale training and evaluation, we curate Dynamic RealEstate10K (D-RE10K), a real-world dataset of 15K casually captured dynamic sequences, and D-RE10K-iPhone, a paired transient and clean benchmark for sparse-view transient-aware NVS. Experiments show that WildRayZer consistently outperforms optimization-based and feed-forward baselines in both transient-region removal and full-frame NVS quality with a single feed-forward pass.
WildRayZer is a self-supervised framework for novel view synthesis in dynamic environments that uses analysis-by-synthesis to handle moving cameras and objects through motion masking and gradient gating.
novel view synthesis dynamic environments multi-view consistency ghosting hallucinated geometry pose estimation analysis-by-synthesis static renderer residuals pseudo motion masks motion estimator input tokens loss gradients cross-view background completion real-world dataset dynamic sequences transient regions feed-forward pass optimization-based baselines feed-forward baselines