The paper addresses the challenge of efficiently reconstructing 3D structures from casual dynamic videos, a problem where existing methods are slow or not applicable to standard videos. This paper introduces a fast, learning-based approach to infer 3D structure and camera positions from these videos through a single feed-forward pass. ----- 📌 This paper smartly shifts from raw pixels to point tracks. This input abstraction enables the model to learn motion patterns, not just scene specifics, boosting generalization to new videos. 📌 The architecture ingeniously incorporates symmetries via attention. Temporal and point permutation equivariance is enforced, making the network inherently geometrically aware and efficient. 📌 Low-rank motion constraint is key. By predicting a basis and coefficients, the model simplifies dynamic scene representation, making unsupervised 3D learning from 2D tracks feasible. ---------- Methods Explored in this Paper 🔧: → The paper introduces \ourmethod, a learning-based approach for fast 3D reconstruction from casual videos. → \ourmethod\ takes 2D point tracks extracted from videos as input, instead of raw images. This design choice aims to improve generalization across different video types. → A novel neural network architecture is designed, considering the symmetries of point track data. → The architecture incorporates permutation symmetry across tracked points and time-translation symmetry across video frames. → The network uses transformer layers with self-attention mechanisms, alternating attention between the time and track dimensions. → To address the ill-posed nature of 3D reconstruction from 2D, a low-rank movement assumption is integrated. → The network predicts a set of basis point clouds, and the dynamic 3D structure is represented as a linear combination of these bases. → The first basis is modeled as a static approximation to aid camera pose estimation. → The method is trained in an unsupervised manner using reprojection error as the primary loss function. ----- Key Insights 💡: → Point tracks are more effective input representations than raw pixels for learning generalizable motion patterns from casual videos. → Incorporating symmetry considerations into the network architecture improves performance and respects the inherent structure of point track data. → Enforcing a low-rank structure on the predicted 3D motion regularizes the solution and makes the ill-posed problem more tractable. → Using a static basis approximation and motion level values helps in disentangling camera motion and dynamic object motion. ----- Results 📊: → \ourmethod\ achieves up to 95% runtime reduction compared to state-of-the-art methods. → It demonstrates comparable 3D reconstruction accuracy to existing methods on pet videos. → On pet videos, it achieves depth Absolute Relative difference of 0.11 for dynamic points and 0.08 for all points. → The method generalizes well to out-of-domain videos with different object categories, maintaining competitive performance. → On out-of-domain videos, it achieves depth Absolute Relative difference of 0.05 for dynamic points and 0.03 for all points after fine-tuning.

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quite unfortunate that in 2024 I still have to rebut like "We are aware of these concurrent methods. XXX will be presented at CVPR24, and YYY recently appeared on arXiv (likely to be an ECCV 2024 submission). We will compare \ourmethod with them in the extension of our work."

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論文書くフォーマットを大学で習えたのは幸せだったと思うIntro/RelatedWork/OurMethod/Experiment/Concolutionのフォーマットが優秀すぎてなんにでも使えるからなー。

therefore, in order to compare ourmethod with the best existing methods, we elected to trainthe supervised models on CelebA HQ instead of FFHQ. 2/2

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