JanusMesh is a fast, zero-shot framework for generating dual-semantic 3D illusions using cross-space denoising, completing in 3-5 minutes with high geometric and semantic fidelity.

Siang-Ling Zhang, Huai-Hsun Cheng, Tsung-Ju Yang et al.

UNIEGO employs proxy-mediated hierarchical distillation from nine heterogeneous teachers to unify egocentric video representations, achieving state-of-the-art results.

Wenhao Chi, Arkaprava Sinha, Dominick Reilly et al.

Proposes Spatially Speculative Decoding (SSD), leveraging 2D spatial prediction to accelerate autoregressive image generation by up to 13.3×.

Shilong Xiang, Zirui Zhang, Lijun Yu et al.

CalTennis is a large multi-view tennis video dataset with over 11 million frames, used to evaluate monocular-to-3D pose estimation, highlighting challenges in depth and foot contact accuracy.

Ilona Demler, Xinran Xie, Blake Werner et al.

UniAR introduces a unified autoregressive framework with a single discrete visual tokenizer, achieving state-of-the-art results in image generation and understanding.

Wujian Peng, Lingchen Meng, Yuxuan Cai et al.

EventDrive integrates event cameras with vision-language models, significantly improving perception, understanding, prediction, and planning in autonomous driving.

Dongyue Lu, Rong Li, Ao Liang et al.

Proposes Qwen-RobotWorld, a language-conditioned video world model using double-stream MMDiT and 8.6M embodied video-text pairs, achieving top performance on multiple benchmarks.

Jie Zhang, Xiaoyue Chen, Anzhe Chen et al.

MeshLoom is a feed-forward non-rigid mesh registration network that reconstructs vertex deformations across sequences within seconds, outperforming state-of-the-art methods.

Jianqi Chen, Jiraphon Yenphraphai, Xiangjun Tang et al.

This study identifies a small set of attention heads—gaze heads—in VLMs that causally track the current description region, enabling effective inference-time control via attention masks.

Rohit Gandikota, David Bau

Introduces OmniVideo-100K, a large-scale dataset with structured scripts and evidence chains, boosting audio-visual reasoning by up to 20.59%.

Xinyue Cai, Chaoyou Fu, Yi-Fan Zhang et al.

Introducing RATS (Register Attention Transformers), which self-supervisedly discovers part-level structures with N learnable registers, achieving +12 mIoU on five segmentation benchmarks.

Timing Yang, Predrag Neskovic, Jansen Seheult et al.

Instruct-Particulate employs large-scale heterogeneous datasets and instruction-guided neural networks to efficiently predict 3D articulated structures, significantly improving generalization.

Ruining Li, Yuxin Yao, Matt Zhou et al.

InterleaveThinker employs a multi-agent framework with a planner and critic, achieving high-quality interleaved text-image generation with step-wise reinforcement learning, improving performance on benchmarks by over 50%.

Dian Zheng, Harry Lee, Manyuan Zhang et al.

Proposes Modality Forcing, a post-training method enabling a single DiT model to jointly generate image and sparse depth data, achieving 57% reduction in AbsRel and scaling with model size.

Bardienus Pieter Duisterhof, Deva Ramanan, Jeffrey Ichnowski et al.

SpatialClaw employs code as an action interface, achieving 59.9% average accuracy across 20 spatial reasoning benchmarks, outperforming recent models by 11.2%.

Seokju Cho, Ryo Hachiuma, Abhishek Badki et al.

Flex4DHuman employs relative camera-pose encoding within a diffusion framework to synthesize synchronized multi-view videos from monocular or sparse inputs, surpassing prior methods without explicit geometry priors.

Jen-Hao Cheng, Yipeng Wang, Hao Zhang et al.

EWSegNet combines spatial and spectral features for efficient waste segmentation in cluttered backgrounds, achieving high accuracy with low computational cost.

Mamoona Javaid, Mubashir Noman, Abdul Hannan et al.

VLGA introduces a dense 3D geometry expert supervised by LiDAR pointmap reconstruction, achieving state-of-the-art safety and driving scores in autonomous driving benchmarks.

Jin Yao, Dhruva Dixith Kurra, Tom Lampo et al.

Proposes Turbo-Inference, an iterative inference strategy leveraging detection-segmentation feedback, improving COCO and Cityscapes mAP by over 1% without retraining.

Zhen Zhao, Gang Zhang, Xiaolin Hu et al.

This paper introduces a metadata-aware multi-prompt reasoning framework for zero-shot accident understanding, achieving a 15% improvement in harmonic mean score on CVPR benchmark.

Tarandeep Singh, Soumyanetra Pal, Soham Biswas et al.