Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

Gerontology

https://doi.org/10.1007/978-3-658-06518-8nificantly influences how people move. However, existing motion models typically overlook these differences, using a normalized, average body instead. This results in a homogenization of motion across human bodies, with motions not aligning with their physical attributes, thus limiting diversity. To

甜瓜

Gloria Phillips-Wren,Sueanne McKniff the varied functionalities required by different 3D applications often necessitate diverse NeRF models with various pipelines, leading to tedious NeRF training for each target task and cumbersome trial-and-error experiments. Drawing inspiration from the generalization capability and adaptability of

侵略

狗窝

Murugan Anandarajan,Teresa D. Harrisonmethods modify softmax scores fine-tuned on outlier data or leverage intermediate feature layers to identify distinctive patterns between In-Distribution (ID) and OOD samples. Other methods focus on employing diverse OOD samples to learn discrepancies between ID and OOD. These techniques, however, a

迫击炮

Hooman Farzaneh,Eric Zusman,Yeora Chaeending NeRFs to model dynamic objects or object articulations remains a challenging problem. Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories,

narcotic

glomeruli

https://doi.org/10.1007/978-981-16-0135-4ntrast to mining spatial relations within images as prompt, which leads to characteristics of different frequencies being neglected and further remaining subtle or undetectable artifacts in the restored image, we develop a .requency .mpting image restoration method, dubbed ., which can effectively p

淘气

Aligning Organizations Through Measurementr, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and ren

流行

繁荣中国

H. Beitter,K.-G. Bergner,J. SchormüllerTransformers (NATs) are able to synthesize images with decent quality in a small number of steps. However, NATs usually necessitate configuring a complicated generation policy comprising multiple manually-designed scheduling rules. These heuristic-driven rules are prone to sub-optimality and come wi