arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.
By Sangwoo Jo, Donggeun Ko, Jayeon Kang, Youngsang Kwak, Jaehwa Kwak, Sungjoon Choi
3D Gaussian Splatting (3DGS) has achieved remarkable success in real-time novel view synthesis, yet it suffers from severe overfitting under sparse-view settings due to insufficient geometric constraints. While recent methods introduce monocular depth priors to mitigate this, they inherently struggle with scale ambiguity and cross-view inconsistency, leading to defective geometry.
arXiv:2607. 10140v1 Announce Type: cross Abstract: Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation.
By Yuang Meng, Chenyang Wu, Xianshun Liu, Chun-Le Guo, Zichen Liang, Lina Lei, Jie Liang, Hui Zeng, Chongyi Li, Lei Zhang
Existing iterative stereo matching methods primarily adopt two types of correspondence representation: explicit matching search via correlation volumes and local residual refinement via warped features, yet the two remain separately modeled. We propose WAVE-Stereo, built on a core insight: correlation volumes and feature warping provide complementary matching cues.
arXiv:2606. 27584v1 Announce Type: cross Abstract: 3D scene inpainting is essential for reconstructing areas corrupted by occlusions or limited viewpoints.
By Hana Kim, Minje Kim, Tae-Kyun Kim
We propose Symmetric Nonlinear Motion-guided Generative Video Frame Interpolation (SNM-VFI), a training-free framework for motion-controllable generative video frame interpolation with pre-trained optical flow and video diffusion models. Unlike conventional diffusion-based VFI methods that synthesize intermediate frames from random noise, SNM-VFI guides the generative process with correspondence-aware frames produced by a symmetric nonlinear motion model.