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提出 ALLOT：一种训练自由的块稀疏注意力分配方法，通过细胞探测与轨迹校准在延迟预算内优化视频扩散 Transformer 各层-步的稀疏度，在 HunyuanVideo 上实现 11.8 dB PSNR 提升。
RealMe 是一个基于高分辨率视频扩散模型的统一框架，能从稀疏、低分辨率的多视角采集生成 2K 分辨率且 4D 一致的沉浸式体积视频，通过布局感知注意力和频域潜空间监督，在 DNA-Rendering 基准和自建 RealVV 数据集上超越现有方法。
AffordanceWAM 提出一种感知可供性（affordance）的生成式世界-动作模型，利用人类视频中的可供性表征联合预测未来场景与机器人动作，无需人类动作标注即可实现人类到机器人的迁移，并在 RoboCasa、CALVIN 等基准上取得一致提升。
PPMem is a video world model that uses compact plane primitives as persistent 3D memory, jointly generating long coherent videos with explicit metric depth, normals, and exportable surfaces while outperforming prior 3D-grounded world models on DL3DV and RealEstate10K.
AcrossVAM2.0 提出一种冲突感知的点对应监督方法，用于参数高效的视频扩散模型适配，通过置信度加权跟踪头、零初始化残差反馈和非对称 PCGrad，在保持预训练外观先验的同时提升对应一致性，但并不均匀改善视频质量。
Velox 提出一种免训练的注意力路由器，利用相机视锥切片的几何对应关系进行区域级上下文选择，从而加速长视频生成中的 KV 缓存处理。在第 48 个 chunk 时注意力加速 4.36 倍、去噪加速 2.43 倍，且在 WBench 导航基准上几乎不损失质量。
GeoFix提出了一种统一框架，通过单一几何奖励提升4D生成中的几何一致性和视觉质量，该奖励衡量静态区域的跨视角深度一致性，并针对视角重叠效应进行校准，实现了WorldScore 3D一致性提升39%和VBench质量提升4%。
ForcingBooster 提出一种自适应逐 patch 去噪框架，根据视频块难度动态分配去噪步数，在保持画质的同时将总去噪步数减少约 50%，加速自回归视频扩散模型的生成。
论文提出 Self Gradient Forcing Plus（SGF+），通过为上下文写入与去噪角色解耦参数并保留因果注意力交互，显著提升自回归视频生成的视觉质量与长时一致性，仅用5秒训练片段即可支持长达24小时的连续生成。
CoRe introduces a co-evolving latent reward framework that continually refits the reward model on the generator's current samples while anchoring to real-video preferences, preventing latent reward hacking and quality collapse in video diffusion models like Wan2.1-T2V-1.3B.
The paper introduces modal kinetic typography, which animates vector glyphs by building motion from their natural vibration modes via a finite-element eigenproblem, with a frozen video diffusion model supervising mode amplitudes and phases for smooth, legible, concept-expressive letter animation.
Proposes SplitMoE, a split-role sparse architecture to break uniformity in mixture-of-experts for scaling video diffusion models, improving convergence speed and video generation quality.
FlashForward introduces a method for faster autoregressive video diffusion by reusing in-flight KV cache and using clean anchors, achieving speedups up to 2.92x while maintaining quality.
Mask Forcing mitigates mode collapse in autoregressive video diffusion distillation by injecting masked cleaner signals during self-rollout, improving visual quality without extra training data.
Ring Forcing is an autoregressive video diffusion framework that enhances long-term memory for precise minute-long video coherence through ring-structured training, history compression, and sparse rotary embeddings.
4DAnyone reconstructs 4D humans from monocular video by generating multiview-consistent videos and lifting them into 4D Gaussian Splatting, using reference and target context designs to address scaling bottlenecks.
AnyTalk generates 3D speech animations for arbitrary characters without requiring animation data by adapting video diffusion models through character-specific fine-tuning and optimizing blendshape parameters, with a real-time distilled variant.
Marionette introduces a world model that explicitly predicts 3D articulated states for interactive games, decouples geometry rendering with a zero-parameter renderer, and uses diffusion for appearance synthesis, improving controllability and long-horizon consistency.
SCoPE is a model from TencentARC that adds camera sightlines as positional coordinates to a pretrained video diffusion transformer, enabling camera trajectory control while preserving the image-to-video prior. The release includes a self-contained checkpoint for Wan2.2-I2V-A14B inference.
LiveAnimate presents a 14B-parameter video diffusion transformer enabling real-time, long-form pose-driven human animation with stable streaming and constant memory usage via PR-Sink attention. Achieves 19.63 FPS inference on two H100 GPUs, maintaining quality over three-minute streams.