ActionSplice uses Counterfactual State Transport to enable in-flight action editing, reducing LPIPS by up to 75.9% and speeding up sampling 2.73×.

Pardis Taghavi, Tingyu Guo, Jonas Lossner et al.

FlexMoGen framework generates high-quality human motions from language and style references.

Kai Weixian Lan, Bodie Criswell, Briana Fedkiw et al.

MyBuddy significantly improves performance on the BuddyVQA benchmark through multimodal chain-of-thought reasoning.

Hangyu Qin, Junbin Xiao, Shenglang Zhang et al.

GAN-Blot: Generates 46K synthetic WB images, enhancing control over protein-band structure and visual style.

Hao-Chiang Shao, Fong-Yi Lin, Te-An Chien et al.

A training-free large-scale scene mesh generation framework combining local continuity and global appearance consistency, improving geometric and visual fidelity.

SangEun Lee, Wonseok Chae, Hoyoung Yoo et al.

Proposes MovieGrid, a spatial grid-based post-training method, increasing multi-shot video generation by 6.05× in a 1616-frame video.

Jiawei Mao, Haoqin Tu, Hardy Chen et al.

A scale–accuracy response framework finds that stronger ImageNet-1K classifiers tolerate smaller inputs: Pearson r=-0.890.

Anish Monsley Kirupakaran

UniMate uses a topology-aware diffusion transformer to animate diverse skeletons, outperforming existing methods.

Linzhan Mou, Jiahui Lei, Zhiyang Dou et al.

CrossDepth improves multi-view depth estimation accuracy and consistency using geometry-constrained attention.

Samer Abualhanud, Max Mehltretter

Ref-GeNVS is a training-free method for generating reflection-consistent novel views in mirror scenes.

GeonU Kim, Shin Dong-Yeon, Tae-Hyun Oh

Proposed H-BAC framework combines quantization and knowledge distillation, achieving 95.13% accuracy and 54.5x model compression.

Mahadev Sunil Kumar, Bhavika Gondi, Desaisetty Venkata Satya Sai Swapnith et al.

AdaGate-DF achieves efficient deepfake detection in low-resolution settings with AUC of 0.9370 on Celeb-DF.

Vaishnavi Sen, Cody Laurie, Rashida Hasan

Principia evaluates physical reasoning in videos via relational consistency across 8 phenomena, independent of calibration, with scores below 0.42 for SOTA models.

Varun Varma Thozhiyoor, Shivam Tripathi, Venkatesh Babu Radhakrishnan et al.

VT-Contrast enhances temporal understanding in VideoLMs by contrasting video tokens, significantly improving benchmarks like TOMATO.

Yumeng Shi, Quanyu Long, Yin Wu et al.

Proposed CORE framework uses reranker distillation with Rank-KL to improve compositional reasoning, achieving 82.7% accuracy.

Tingyu Song, Mingxin Li, Yanzhao Zhang et al.

Introduces WorldReward, a VLM-based pairwise preference reward model combining action consistency and visual quality, validated on a new benchmark.

Yibin Wang, Zehan Wang, Junshu Tang et al.

Using Orthogonal Matching Pursuit for frame selection in long videos, improving by 6.9 points.

Prakhar Khatri

Describes image subset differences using natural language, introducing AD-Diff Bench benchmark.

Julian Truetsch, Felix Hauser, Christoph Stiller et al.

CoFiE achieves a state-of-the-art accuracy-efficiency trade-off in video understanding benchmarks, reaching 78.86% accuracy with coarse-to-fine evidence selection.

Jing Jiang, Yiran Ling, Ruonan Li et al.

Drive-HWM employs a hierarchical slow-fast world model combining multi-step future representations with real-time action prediction, significantly improving autonomous driving performance.

Zhaoxin Fan, Tianbao Zhang, Wenjun Wu et al.