Luc Van Gool

Powerful 2D VLMs can be transformed into geometry-aware 3D programmers capable of complex manipulation and generation tasks, simply by grounding their outputs in a canonical coordinate system and employing adaptive feedback loops.

Distilling multi-pass top-down keypoint detection into a single full-image forward pass breaks the latency bottleneck of generalist vision models while securing 53.1 PQ on COCO panoptic segmentation.

Exocentric-to-egocentric video synthesis no longer requires explicit 3D geometric attention or human-written prompts; scaled diffusion can implicitly resolve complex viewpoint shifts using only internal representations and a lightweight depth prior.

CoRef-GS is proposed, a cooperative referring Gaussian splatting framework that constructs local open-vocabulary instance-aware Gaussian maps, then aligns partially overlapping maps with a cross-agent alignment module by geometric and semantic consistency, and grounds queries using a view-conditioned mask relation graph.

The authors', a persistent language field for structured Gaussian scenes that requires persistent semantic ownership, conserved evidence, and a hierarchy that balances stability, detail, and representation cost, is introduced.

Clip-level captions compress away the continuous visual dynamics needed for true long-horizon reasoning; Kairos restores this signal with dense, time-resolved annotations tracking actions, entities, and attributes across 10-to-30-minute videos.

Sparse annotations can yield results nearly indistinguishable from dense ones, with SA-VIS achieving over 1% improvement in AP on state-of-the-art benchmarks.

Get up to 20% faster ViT inference by hot-swapping certain attention heads for depthwise convolutions – without tanking accuracy.

Bridging the gap between third-person and first-person video generation is as simple as interpolating the videos, revealing that spatio-temporal discontinuities are the real bottleneck.

Removing an object from a video isn't just about inpainting what's behind it; VOID ensures the *downstream physics* still make sense.

Autonomous vehicles can now better identify the unexpected, thanks to a new method that boosts out-of-distribution detection by up to 20% without retraining.

The field of video understanding is rapidly shifting from isolated pipelines to unified models capable of adapting to diverse downstream tasks, demanding a re-evaluation of current approaches.