Phenaki

Generates coherent videos up to 2+ minutes in length from natural language text prompts using a hierarchical diffusion architecture that decomposes long narratives into keyframe sequences and interpolates temporal coherence between frames. The model uses a two-stage approach: first generating sparse keyframes that capture semantic milestones from the text, then densifying intermediate frames through learned motion patterns. This enables multi-scene narratives with maintained object identity and spatial consistency across extended sequences, addressing the fundamental challenge of temporal coherence that limits competing text-to-video systems to 15-30 second clips.

Maintains consistent object identity, spatial relationships, and character appearance across multiple scenes and scene transitions within a single generated video. The model uses a scene-graph-aware attention mechanism that tracks semantic entities (characters, objects, locations) across the narrative timeline, ensuring that a character introduced in scene 1 maintains consistent visual appearance in scene 3 despite intervening scenes. This is implemented through cross-scene attention layers that bind entity embeddings across temporal boundaries, preventing the identity drift and appearance inconsistencies that plague naive sequential generation approaches.

Generates smooth, physically plausible motion between keyframes by learning motion patterns from training data rather than simple linear interpolation. The model predicts optical flow and motion vectors between sparse keyframes, then uses these predictions to synthesize intermediate frames with natural acceleration, deceleration, and object interactions. This approach avoids the jittery, unrealistic motion that results from naive frame interpolation, producing videos where characters move fluidly and objects interact with apparent physical consistency across the 2+ minute duration.

Automatically identifies and extracts semantic milestones from natural language text descriptions, converting narrative structure into sparse keyframe specifications that guide video generation. The model uses a language understanding component to parse text, identify scene boundaries, key actions, and visual transformations, then maps these to frame indices and visual descriptions. This enables the hierarchical generation approach where keyframes capture semantic intent from the text, and intermediate frames are synthesized to connect them, rather than attempting to generate every frame from scratch.

Generates video frames using a diffusion model architecture that operates in a learned latent space, with temporal consistency constraints that couple adjacent frames through attention mechanisms and temporal loss functions. The model iteratively denoises latent representations while enforcing temporal smoothness through cross-frame attention and optical flow constraints, preventing the frame-to-frame jitter and inconsistency typical of independent frame generation. This is implemented as a conditional diffusion process where each frame generation is conditioned on previous frames and the narrative context, creating a Markovian dependency structure that maintains coherence.

Provides visibility into video generation quality through research-oriented evaluation metrics and artifact characterization, documenting known limitations such as motion inconsistencies, blurriness, and diffusion artifacts. While not a user-facing capability in the traditional sense, Phenaki's research documentation explicitly characterizes output quality, enabling researchers and evaluators to understand failure modes and assess suitability for specific use cases. This includes analysis of temporal coherence metrics, perceptual quality scores, and qualitative artifact descriptions that inform expectations.