4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video

4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/4dequine-disentangling-motion-and-appearance-for-4d-equine-reconstruction-from-monocular-video

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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Canonical ID 4dequine-disentangling-motion-and-appearance-for-4d-equine-reconstruction-from-monocular-video | Route /signal-canvas/4dequine-disentangling-motion-and-appearance-for-4d-equine-reconstruction-from-monocular-video

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MCP example

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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: 4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video

PDF: https://arxiv.org/pdf/2603.10125v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Signal Canvas receipt window

Watch and verify: 4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video

/buildability/4dequine-disentangling-motion-and-appearance-for-4d-equine-reconstruction-from-monocular-video

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Subject: 4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
we propose a novel framework called 4DEquine by disentangling the 4D reconstruction problem into two sub-problems: dynamic motion reconstruction and static appearance reconstruction.
Implicationpartial
The abstract explicitly states the proposed framework's core idea of disentanglement.
Verificationpartial
partial
Evidencepartial
For motion, we introduce a simple yet effective spatio-temporal transformer with a post-optimization stage to regress smooth and pixel-aligned pose and shape sequences from video.
Implicationpartial
The abstract clearly describes the components used for motion reconstruction.
Verificationpartial
partial
Evidencepartial
For appearance, we design a novel feed-forward network that reconstructs a high-fidelity, animatable 3D Gaussian avatar from as few as a single image.
Implicationpartial
The abstract details the approach for appearance reconstruction.
Verificationpartial
partial
Evidencepartial
To assist training, we create a large-scale synthetic motion dataset, VarenPoser, which features high-quality surface motions and diverse camera trajectories
Implicationpartial
The abstract describes the characteristics of the VarenPoser dataset.
Verificationpartial
partial
Evidencepartial
as well as a synthetic appearance dataset, VarenTex, comprising realistic multi-view images generated through multi-view diffusion.
Implicationpartial
The abstract describes the VarenTex dataset and its generation method.
Verificationpartial
partial
Evidencepartial
While training only on synthetic datasets, 4DEquine achieves state-of-the-art performance on real-world APT36K and AiM datasets, demonstrating the superiority of 4DEquine and our new datasets for both geometry and appearance reconstruction.
Implicationpartial
The abstract explicitly states the performance achievement and the training condition.
Verificationpartial
partial
Evidencepartial
Previous mainstream 4D animal reconstruction methods require joint optimization of motion and appearance over a whole video, which is time-consuming and sensitive to incomplete observation.
Implicationpartial
The abstract highlights the limitations of prior methods, providing context for the proposed work.
Verificationpartial
partial

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4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video

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