🎬 Video Generation¶

🎞️ ECCV2026 · 18 paper notes

📌 Same area in other venues: 📷 CVPR2026 (182) · 🔬 ICLR2026 (98) · 💬 ACL2026 (4) · 🧪 ICML2026 (32) · 🤖 AAAI2026 (11) · 🧠 NeurIPS2025 (23)

🔥 Top topics: Diffusion Models ×7 · Video Generation ×6 · Reasoning ×2

Accelerating Text-to-Video Generation with Calibrated Sparse Attention: CalibAtt compiles input-stable block sparsity and spatial row repetition into two attention execution paths without updating model weights, reducing Wan 2.1 14B's 720p generation latency from 1244 to 785 seconds while retaining comparable VBench performance.
Aligning Human Sense: Calibrated Distributional Reward Learning for Video Generation: The paper connects consensus-based annotation calibration, multidimensional reward learning, and Wasserstein-constrained GRPO, improving action pairwise accuracy excluding ties from 80.30% to 83.33% and the reported aggregate scores of three video generators, although not every individual metric improves.
Anchor Forcing: Anchor Memory and Tri-Region RoPE for Interactive Streaming Video Diffusion: Anchor Forcing addresses evidence loss at prompt switches with anchor-guided re-caching and positional distribution shift during long rollouts with tri-region RoPE, improving the LongLive baseline from 26.37 to 73.00 in dynamic degree and from 78.92 to 82.25 in quality on interactive 60-second videos.
AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories: Instead of fusing historical views into one global point cloud, AnchorWeave retrieves complementary local point clouds, renders multiple anchor videos, and learns to reconcile them, improving its CogVideoX variant from 19.01 to 20.96 dB PSNR and from 0.6145 to 0.6727 SSIM over single-anchor conditioning under partial-revisit evaluation.
AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation: AnyFlow replaces endpoint-only video distillation with transitions across arbitrary time intervals, then corrects student rollouts using three-segment shortcut simulation and distribution matching, reaching 84.05 VBench at 4 NFEs and 84.41 at 32 NFEs with its 14B causal model.
ART-VSR: Adaptive Rectified Trajectories for One-Step Video Super-Resolution: ART-VSR estimates how far each token should travel along a generative path, then rectifies its latent starting state accordingly, enabling one-step restoration with a Wan2.1 video prior; it achieves 0.197 LPIPS on REDS under the authors' unified re-evaluation protocol, without dominating every fidelity or perceptual metric.
Benchmarking Scientific Understanding and Reasoning for Video Generation using VideoScience-Bench: VideoScience-Bench places video generators in experimental scenarios requiring combinations of scientific concepts, evaluates their outputs with expert ratings and an evidence-grounded VideoScience-Judge, and shows that visual coherence is not scientific correctness, while checklist-based judging reaches a Spearman correlation of 0.96 with expert rankings.
Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation: CineCrew uses FilmDSL to organize narrative intent, cinematic staging, assets, and repair feedback into an executable production workflow without retraining video models, improving character, prop, and story continuity on 20 MovieBench narratives without leading every visual-quality or physics metric.
Beyond Inpainting: Unleash 3D Understanding for Stable Camera-Controlled Video Re-rendering: DepthDirector controls novel viewpoints with warped depth and preserves appearance and motion through the source video, avoiding erroneous textures inherited from warped RGB; its full model achieves RE 1.010, TE 0.053, and CamMC 1.434 under the absolute-camera protocol.
ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation: ConfCtrl uses endpoint-conditioned video interpolation as a generative prior, changes the starting distribution of rectified flow with confidence-weighted projected point-cloud latents, and refines camera conditioning through predict-update modules, achieving 15.54 dB PSNR, 0.143 m translation error, and 0.103 rad rotation error on CO3D-Hydrant.
Consistent Video-to-Video Translation via Explicit Correspondences: vid2vid-long uses the current input block to retrieve matching outputs generated much earlier, then conditions video translation through per-token correspondence cross-attention, reducing five-step Rolling Forcing LPIPS on VACE-Bench from 0.427 to 0.240 while also reducing throughput from 10.99 to 8.10 FPS.
Control-DINO: Feature Space Conditioning for Controllable Video Diffusion: Control-DINO controls frozen CogVideoX with full-channel dense DINOv3 features, reduces source-appearance copying by pairing original features with multiple augmented appearances, reaches a COLMAP registration rate of 0.9932 on stride=16 T&T appearance transfer, and extends the same feature interface to 3D-guided generation.
ControlHair: Synergizing Physics Simulator and Video Diffusion for Controllable Dynamic Hair Rendering: ControlHair delegates hair motion to a physics simulator and uses per-frame strand maps and human poses to condition video diffusion, reaching 17.15 dB PSNR on reconstruction of ten VEO 2 videos; this measures control-signal adherence rather than end-to-end physical accuracy.
Controlling Motion Transfer in Diffusion Transformers via Attention Heads: HALO identifies motion-specific and structure-specialized heads in video DiTs, supervises generation latents with semantically refined displacements, and preserves layout through low-entropy reference value features, improving main-benchmark motion fidelity MF from DiTFlow's 59.6 to 66.2 without updating model parameters while maintaining target-text alignment.
Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency: Cycle-World asks whether a generated video chunk can reconstruct its predecessor, using this constraint for both training and inference-time latent correction to reach a 60-second VBench total of 82.88 and improve physical consistency, without establishing drift-free generation at arbitrary lengths.
DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation: DCARL jointly generates sparse keyframes spanning the target timeline, then constrains segment-wise interpolation with noisy keyframes and clean history, reducing FVD from SEVA's 548.0 to 203.7 and camera trajectory error ATE from 0.294 to 0.237 on 32-second OpenDV-YouTube videos.
DeforM: Reasoning-Guided Physics-Aware Video Generation via Spatial-Temporal Masking: DeforM converts vision-language reasoning about which objects should deform into spatial-temporal masks, then guides Wan2.2 through either training-free attention modulation or learned dual-attention injection, improving the same-backbone physical score from 3.49 to 4.40 on a deformation test set; this measures evaluator-assessed plausibility, not guaranteed compliance with physical laws.
DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models: DiffHDR treats clipped highlights and noise-obscured shadows in a single 8-bit LDR video as radiance-inpainting regions, uses Log-Gamma mapping to generate HDR in the latent space of a frozen LDR video VAE, reaches 6.89 FOVVDP versus SingleHDR's 6.56 on Cinematic Video, and supports text- or reference-guided reconstruction.