Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting
Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstruction. Instead of focusing on modeling motion, our goal is to create a frozen scene while strategically preserving subtle dynamics to enable user-controlled instant selection. To achieve this, we introduce a novel application of dynamic Gaussian splatting: the scene is modeled dynamically, which retains nearby temporal variation, and a static scene is rendered by fixing the model's time parameter. However, under this usage, monocular capture with sparse temporal supervision introduces artifacts like ghosting and blur for Gaussians that become unobserved or occluded at weakly supervised timestamps. We propose Splannequin, an architecture-agnostic regularization that detects two states of Gaussian primitives, hidden and defective, and applies temporal anchoring. Under predominantly forward camera motion, hidden states are anchored to their recent well-observed past states, while defective states are anchored to future states with stronger supervision. Our method integrates into existing dynamic Gaussian pipelines via simple loss terms, requires no architectural changes, and adds zero inference overhead. This results in markedly improved visual quality, enabling high-fidelity, user-selectable frozen-time renderings, validated by a 96% user preference. Project page: https://chien90190.github.io/splannequin/
693247cb6d1060ca587a26e2
Hao-Jen Chien
claimed_verified
2025-12-05T08:29:29.136Z
6784959669a180a9949ec5d9
/avatars/b1fcc9a00a8e6e2baaa338c4b04ea9e2.svg
Hao-Jen Chien
chien90190
user
chien90190
693247cb6d1060ca587a26e3
Yi-Chuan Huang
claimed_verified
2025-12-10T09:10:16.461Z
67178582bc4492cad19a1f14
/avatars/f2481c0c70a857a862d887beb05c428e.svg
Yi-Chuan Huang
YiChuanH
user
YiChuanH
693247cb6d1060ca587a26e4
Chung-Ho Wu
693247cb6d1060ca587a26e5
Wei-Lun Chao
693247cb6d1060ca587a26e6
Yu-Lun Liu
Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting
Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstruction. Instead of focusing on modeling motion, our goal is to create a frozen scene while strategically preserving subtle dynamics to enable user-controlled instant selection. To achieve this, we introduce a novel application of dynamic Gaussian splatting: the scene is modeled dynamically, which retains nearby temporal variation, and a static scene is rendered by fixing the model's time parameter. However, under this usage, monocular capture with sparse temporal supervision introduces artifacts like ghosting and blur for Gaussians that become unobserved or occluded at weakly supervised timestamps. We propose Splannequin, an architecture-agnostic regularization that detects two states of Gaussian primitives, hidden and defective, and applies temporal anchoring. Under predominantly forward camera motion, hidden states are anchored to their recent well-observed past states, while defective states are anchored to future states with stronger supervision. Our method integrates into existing dynamic Gaussian pipelines via simple loss terms, requires no architectural changes, and adds zero inference overhead. This results in markedly improved visual quality, enabling high-fidelity, user-selectable frozen-time renderings, validated by a 96% user preference. Project page: https://chien90190.github.io/splannequin/
Splannequin is a regularization technique that improves the visual quality of frozen 3D scenes synthesized from monocular videos by addressing artifacts in dynamic Gaussian splatting.
dynamic Gaussian splatting Gaussian primitives hidden states defective states temporal anchoring loss terms inference overhead