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Sol-Engine weekly update announces integration of Sol Attention, a training-free sparse attention method for video diffusion, with full paper coming next week.
Proposes StudioRecon, a method for 4D human-scene reconstruction from low-overlap camera captures, using separate priors for backgrounds (video diffusion) and humans (SMPL), with a recursive enhancement module. Accepted to SIGGRAPH 2026, outperforming prior methods by +1.5 to +5.0 dB PSNR.
Flex-Forcing introduces a unified framework for video diffusion that supports both autoregressive and bidirectional generation modes, offering flexible control for video generation tasks.
LongE2V leverages pre-trained video diffusion priors and introduces Autoregressive Unrolling, Adaptive Context Switching, Reencoding Alignment, and Event Voxel Density Augmentation to achieve high-quality video reconstruction, prediction, and frame interpolation from sparse event streams, outperforming state-of-the-art methods.
LingBot-Video is a 13B sparse-MoE video diffusion transformer (1.4B active) post-trained with RL as an action-conditioned world model, open-sourced with weights and code. It includes a physical-plausibility reward graded by a VLM and frames itself as a policy evaluator and action planner, though closed-loop robot results are absent.
MV-Forcing introduces a diffusion framework that combines temporal and view-wise autoregression to generate long, multi-view consistent videos of dynamic scenes, using a 4D geometric bridge and spatio-temporal distillation to enable arbitrary-length generation from a few-step student model.
ViDiHand leverages pretrained video diffusion model representations to reconstruct 4D hand motion directly from egocentric video frames, outperforming existing methods on ARCTIC, HOT3D, and HOI4D without detectors or optimization.
Enze Xie announces Sol Video Inference Engine, an agent-native, training-free full-stack accelerator for video diffusion that auto-tunes cache, sparse attention, token pruning, quantization, and kernel fusion, achieving >2× end-to-end speedup on large models like 64B Cosmos3-Super and 22B LTX-2.3.
FLAT is a method that directly decodes explicit triangle splats from compressed video diffusion latents in a single forward pass, improving geometric accuracy while enabling fast rasterization and physics-based interaction.
A scalable framework combines self-distillation and reinforcement learning to transfer task-solving abilities from vision-language models to video diffusion models without requiring labeled task-video data.
FadeMem introduces a distance-aware key-value memory consolidation mechanism that organizes historical video data into a temporal hierarchy, improving long-video generation under fixed cache constraints.
Steady-Forcing proposes a memory and training framework to balance spatial stability and motion continuity in long-horizon nature video generation, improving background consistency while sustaining fluid dynamics over multi-minute rollouts.
ByteDance open-sourced Bernini-R, a video diffusion renderer that combines an MLLM-based semantic planner with a DiT-based renderer for unified video generation and editing, achieving top-tier performance on video editing.
τ_0-WM is a unified video-action world model for robotic manipulation that integrates policy learning, video prediction, and action evaluation using a shared video diffusion backbone. It shows superior performance on challenging long-horizon and fine-grained tasks.
LVSA introduces a training-free sparse attention mechanism for video diffusion models, reducing compute up to 3.17x while enabling generation beyond training horizons without quality loss.
VideoMLA replaces per-head KV caches in video diffusion models with a shared low-rank latent and decoupled 3D-RoPE positional keys, reducing per-token KV memory by 92.7% and improving throughput by 1.23x on a B200 while maintaining quality on VBench benchmarks.
This paper identifies a bias in attention weights caused by quantizing keys in KV-cache compression for chunk-wise autoregressive video diffusion, and proposes a per-attention-score correction that removes the bias with negligible overhead, recovering near-BF16 video quality at INT2 quantization.
SCOPE introduces a method for precise action response in FPS games by conditioning transformer blocks in video diffusion models to separate in-scope effects from out-of-scope visual effects without segmentation labels, and presents CrossFPS, a multi-game dataset enabling zero-shot cross-game transfer.
WorldKV is a training-free framework that retrieves and compresses key-value cache chunks to maintain long-term consistency in video diffusion world generation, achieving higher throughput while matching full-memory fidelity.
A novel inference-time method for long video generation using overlapping sliding windows with Tweedie matching and stochastic early-phase sampling to improve temporal consistency and visual quality without additional training.