AI·YouTube CEO Neil Mohan seeks to balance AI-generated content flood with human creativity safeguards, including labeling AI videos, while launching virtual avatar tools and noting 17.5% of shorts may be AI-generated.
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AI·Introduces Flaxeo Image, a local desktop UI for Stable Diffusion C++ that exposes model, hardware, and video options.
AI·A $100 setup using three NVIDIA P102 cards delivers 20 GB VRAM and 448 GB/s memory bandwidth, sufficient for three concurrent LLM users with high context and better speeds than costlier lower-VRAM cards.
AI·LongE2V leverages pre-trained video diffusion models to reconstruct, predict, and interpolate event-based videos, achieving high data efficiency and superior perceptual quality through autoregressive unrolling and adaptive context switching.
AI·OpenCoF enables reasoning in video generation through temporal frame chains distinct from Chain-of-Thought, addressing training data gaps.
AI·SAM-MT extends Segment Anything 2 for real-time interactive multi-target video segmentation, improving FPS by jointly processing multiple targets instead of replicating single-target pipelines.
AI·OPSD-V self-distillation method reduces error accumulation in few-step autoregressive video generators while preserving fast inference.
AI·LingBot-Video, a DiT-based video pretraining method using Mixture-of-Experts, is tailored for embodied intelligence by prioritizing physical realism and computational efficiency over visual creativity in robot control.
AI·LingBot-World 2.0 is an advanced world modeling system with four upgrades: unbounded interaction horizon, real-time 60 fps video support, diverse interactive actions and events, and multi-agent control for multi-player virtual environments.
AI·LingBot-Video is a 13B-parameter sparse-MoE video diffusion transformer (1.4B active) post-trained as an action-conditioned world model with physical-plausibility RL, releasing weights, code, and Diffusers/SGLang integration for robot rollouts.
AI·RoboTALES trains robot policies by learning task-aligned simulated futures with a hierarchical LLM-based planner and VLM-based critic, producing coherent actions that outperform prior methods on long-horizon manipulation benchmarks from RoboCasa and LIBERO10.
The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, slow motion, etc. While Diffusion Transformers (DiTs) exhibit strong performance in video generation, their large parameter sizes and multi-step iterative denoising processes lead to substantial computational overhead, making efficient generation on mobile devices challenging. We propose CineMobile to bridge the gap. In particular, CineMobile adopts…
Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry. Tactile sensing provides these complementary signals, yet tactile data remain costly to collect and hard to generalize across sensors, robots, and tasks. We introduce OmniTacTune, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visu…
We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters. Users can control video generation content at any moment through voice instructions. Vidu S1 supports infinite-length real-time video generation without blurring, drift, or visual distortion. Built with TurboDiffusion and TurboServe, Vidu S1 outputs 540p real-time videos at up to 42 FPS on regular consumer GPUs. Users can upload custom images of real people, anime, and pets, and …
The inherent complexity of video understanding makes it difficult to determine whether Video-LLM benchmark performance stems from visual perception, linguistic reasoning, or knowledge priors. While many benchmarks have emerged to assess high-level reasoning, shared criteria for evaluating video understanding remain largely overlooked. Instead of introducing yet another benchmark, we take a step back to re-examine the criteria for evaluating video understanding. In this work, we introduce Video-…