arXiv:2606. 17584v1 Announce Type: cross Abstract: Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training-free image editing.
By Semin Kim, Jihwan Yoon, Seunghoon Hong
arXiv:2605. 16399v2 Announce Type: replace-cross Abstract: The inversion of diffusion models plays a central role in image editing.
By Barbora Barancikova, Daniil Shmelev, Cristopher Salvi
arXiv:2608.22773v1 Announce Type: new
Abstract: Dynamic 3D Gaussian Splatting (3DGS) achieves photorealistic reconstruction of time-varying scenes, and recent physics-aware extensions improve extrapo...
By Shogo Sato, Takuhiro Kaneko, Shoichiro Takeda, Tomoyasu Shimada, Riku Inoue, Kazuhiko Murasaki, Ryuichi Tanida
FlashRender is a few-step generative rendering framework that quickly retakes a source video along a target camera trajectory. It addresses discretization error by introducing Representation Transformation and Alignment (RETA) to align source-video representations with target-video features, reducing denoising trajectory curvature. The model is further refined with a MeanFlow objective and on-policy flow map distillation, achieving video quality and geometric consistency comparable to multi-step baselines at a 25× lower sampling cost while improving camera controllability.
By Byeongjun Park, Byung-Hoon Kim, Hyungjin Chung
Geometric foundation models, such as the Visual Geometry Grounded Transformer (VGGT), provide strong 3D priors from unposed images. However, such models operate purely in a feed-forward, deterministic regime, \ie~they cannot generate plausible geometry beyond what the input views directly support.
arXiv:2602. 10099v2 Announce Type: replace Abstract: Leveraging representation encoders for generative modeling offers a path for efficient, high-fidelity synthesis.
By Amandeep Kumar, Vishal M. Patel