Mohammad Mahdi

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.

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.

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.