The paper introduces Atomic Dance, a two‑stage framework that first discovers repeatable, semantically labeled atomic movements and then plans and completes dance sequences, achieving more coherent, rhythm‑aligned and editable music‑driven choreography, as demonstrated on the AIST++ benchmark.
VGGRPO introduces a latent‑space geometry model and two 4D rewards—camera motion smoothness and geometry reprojection consistency—to improve geometric consistency in video diffusion models without sacrificing pre‑training generalization, achieving smoother camera paths and coherent scene structures even in dynamic scenarios.
VGGRPO introduces a latent‑space geometry model and two 4D rewards—camera‑motion smoothness and geometry‑reprojection consistency—to eliminate drift and improve structural coherence in video diffusion models without altering their pretrained architecture, achieving state‑of‑the‑art results on static and dynamic benchmarks.
The ECCV 2026 paper by He Kaiming, Andrew Zisserman and collaborators proposes GenCeption, a text‑to‑video model repurposed as a single‑forward DiT‑based vision learner that unifies depth, segmentation, pose and 3D tasks, and evaluates its multi‑task performance against specialized baselines.
ShotStream tackles the high latency and zero‑interaction problems of multi‑shot long video generation by proposing a streaming architecture with a dual‑cache memory, discontinuous RoPE, and a two‑stage self‑forcing distillation, achieving over 25× speedup to 16 FPS on a single H200 GPU and outperforming existing bidirectional and autoregressive models.
TryOnCrafter introduces a camera‑controllable video virtual try‑on framework that builds a renderable 4D proxy to enable free‑view, 360° and bullet‑time effects while preserving structural stability, texture consistency, and realistic motion across arbitrary camera trajectories.
LinStereo replaces ConvGRU with a position‑aware linear attention module, adds a multi‑scale cost volume and monocular depth initialization, cutting Middlebury occlusion error by 37%, outperforming larger models, and achieving strong zero‑shot underwater performance while remaining parameter‑efficient.
ECCV 2026 received 10,473 submissions and accepted 2,883 (27.5%); Gaode contributed six papers spanning computer vision, generative video, and visual‑language navigation, each presenting novel reinforcement‑learning or multimodal frameworks, new datasets, and benchmark results that outperform prior state‑of‑the‑art methods.
