{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_phenaki","slug":"phenaki","name":"Phenaki","type":"model","url":"https://phenaki.video","page_url":"https://unfragile.ai/phenaki","categories":["video-generation","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_phenaki__cap_0","uri":"capability://image.visual.long.form.video.generation.from.text.descriptions","name":"long-form video generation from text descriptions","description":"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.","intents":["Generate multi-minute narrative videos from screenplay or story descriptions without manual frame-by-frame editing","Create long-form content for marketing, educational, or creative projects from text alone","Prototype complex video sequences with multiple scenes and character interactions before live production","Explore how AI handles temporal storytelling and narrative structure in video synthesis"],"best_for":["Researchers evaluating state-of-the-art text-to-video generation architectures","Enterprises with special research access exploring long-form video synthesis capabilities","Content creators prototyping narrative-driven video concepts at the research frontier"],"limitations":["Output exhibits visible diffusion artifacts, motion inconsistencies, and characteristic blurriness typical of current generative video models","Temporal coherence degrades with narrative complexity; longer sequences show accumulated drift in object positioning and lighting","No fine-tuning or style control mechanisms exposed; outputs reflect training distribution without customization","Inference latency scales non-linearly with video length; 2+ minute generation requires significant computational resources and wall-clock time","Limited to research demonstration quality; production-grade reliability and consistency not guaranteed"],"requires":["Special research access or partnership agreement with Google; not available via standard API","Text prompt describing desired video content (English language, narrative structure recommended)","Sufficient computational resources on Google's infrastructure for inference (handled server-side)"],"input_types":["text (natural language descriptions, screenplays, narrative prompts)"],"output_types":["video (MP4 or similar format, resolution and frame rate dependent on model configuration)"],"categories":["image-visual","text-to-video","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_phenaki__cap_1","uri":"capability://image.visual.multi.scene.narrative.coherence.with.object.identity.preservation","name":"multi-scene narrative coherence with object identity preservation","description":"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.","intents":["Generate multi-scene stories where characters maintain consistent appearance and identity across scenes","Create videos with complex spatial relationships that persist across narrative transitions","Ensure objects referenced in text descriptions remain visually consistent throughout the generated video","Build coherent narratives with multiple locations and character interactions without manual consistency correction"],"best_for":["Narrative-driven content creators building story-based videos from text","Researchers studying entity tracking and identity preservation in generative video models","Teams prototyping character-driven content where consistency is critical"],"limitations":["Identity preservation degrades with scene count; 3+ scene narratives show measurable appearance drift","Spatial relationships become ambiguous in complex multi-character scenes; simple 1-2 character narratives perform best","No explicit control over character appearance or style; consistency emerges from training data distribution","Scene transitions may show visual discontinuities despite entity tracking; lighting and background consistency not guaranteed"],"requires":["Text descriptions that explicitly name and reference entities across scenes","Narrative structure with clear scene boundaries and character/object mentions","Access to Phenaki's research API or demonstration interface"],"input_types":["text (narrative descriptions with explicit entity references across scenes)"],"output_types":["video (with consistent entity appearance across scenes)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_phenaki__cap_2","uri":"capability://image.visual.temporal.coherence.through.learned.motion.interpolation","name":"temporal coherence through learned motion interpolation","description":"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.","intents":["Generate videos with smooth, natural-looking motion and character movement","Create content where objects and characters interact with apparent physical plausibility","Avoid jittery or unrealistic motion artifacts in long-form video synthesis","Produce videos suitable for narrative content where motion quality impacts viewer experience"],"best_for":["Content creators prioritizing motion quality and physical plausibility in generated videos","Researchers studying learned motion synthesis and optical flow prediction in generative models","Projects where motion artifacts would significantly degrade perceived quality"],"limitations":["Motion prediction fails for complex interactions (multiple objects colliding, fluid dynamics, cloth simulation)","Learned motion patterns reflect training data distribution; novel or unusual motions may appear unnatural","Accumulated motion error compounds across long sequences; 2+ minute videos show visible motion drift by the end","No explicit control over motion speed, direction, or style; motion emerges implicitly from text description","Motion interpolation adds computational overhead; longer videos with complex motion require proportionally more inference time"],"requires":["Text descriptions that clearly specify desired motion and interactions","Access to Phenaki's inference infrastructure","Patience for inference latency; motion synthesis is computationally expensive"],"input_types":["text (descriptions of desired motion, character actions, object interactions)"],"output_types":["video (with synthesized intermediate frames and motion interpolation)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_phenaki__cap_3","uri":"capability://text.generation.language.semantic.keyframe.extraction.from.narrative.text","name":"semantic keyframe extraction from narrative text","description":"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.","intents":["Convert screenplay or narrative text into structured keyframe specifications for video generation","Automatically identify scene boundaries and key visual moments from text descriptions","Enable efficient video generation by focusing diffusion effort on semantically important frames","Map narrative structure directly to visual structure without manual keyframe specification"],"best_for":["Content creators working from scripts or narrative descriptions","Researchers studying narrative-to-visual mapping and semantic understanding in generative models","Teams building automated video generation pipelines from text"],"limitations":["Keyframe extraction quality depends on text clarity and narrative structure; ambiguous descriptions produce suboptimal keyframe placement","No explicit control over keyframe selection; extraction is automatic and not user-adjustable","Complex narratives with implicit scene transitions may be misinterpreted; explicit scene markers recommended","Semantic understanding is limited to training data distribution; novel narrative structures may not parse correctly","No feedback mechanism to refine keyframe extraction; users cannot iteratively improve results"],"requires":["Natural language text descriptions with clear narrative structure","English language text (other languages not confirmed supported)","Reasonably detailed descriptions; minimal prompts may produce poor keyframe extraction"],"input_types":["text (narrative descriptions, screenplays, story prompts)"],"output_types":["structured data (keyframe specifications with frame indices and visual descriptions)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_phenaki__cap_4","uri":"capability://image.visual.diffusion.based.video.frame.synthesis.with.temporal.consistency","name":"diffusion-based video frame synthesis with temporal consistency","description":"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.","intents":["Generate video frames with diffusion-based quality and diversity while maintaining temporal consistency","Avoid frame-to-frame flicker and jitter that occurs when frames are generated independently","Leverage diffusion model capabilities (high quality, diverse outputs) while preserving video coherence","Synthesize intermediate frames between keyframes with temporal smoothness"],"best_for":["Researchers studying diffusion models applied to video generation","Teams prioritizing output quality over inference speed","Projects where temporal consistency is critical and some latency is acceptable"],"limitations":["Diffusion-based generation is computationally expensive; inference latency scales with video length and desired quality","Output exhibits characteristic diffusion artifacts: blurriness, loss of fine detail, and occasional semantic inconsistencies","Temporal consistency constraints reduce diversity; generated videos may appear overly smooth or lack dynamic variation","No real-time or near-real-time generation possible; 2+ minute videos require minutes to hours of inference time","Latent space representation may lose high-frequency details; output resolution is limited by model architecture"],"requires":["Significant computational resources (GPU/TPU) for inference; typically requires Google's infrastructure","Text descriptions and keyframe specifications as input","Patience for inference latency; batch generation recommended for multiple videos"],"input_types":["text (narrative descriptions)","structured data (keyframe specifications with temporal indices)"],"output_types":["video (latent-space decoded to pixel space, with temporal consistency constraints)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_phenaki__cap_5","uri":"capability://safety.moderation.research.grade.video.quality.assessment.and.artifact.characterization","name":"research-grade video quality assessment and artifact characterization","description":"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.","intents":["Understand and characterize known limitations and artifacts in generated videos","Assess whether Phenaki output quality is suitable for specific research or production use cases","Benchmark Phenaki against other text-to-video models using documented quality metrics","Identify failure modes and edge cases for research purposes"],"best_for":["Researchers evaluating text-to-video generation models and comparing approaches","Teams assessing whether Phenaki is suitable for their specific application","Enterprises considering investment in video generation technology"],"limitations":["Quality assessment is primarily qualitative and research-oriented; no quantitative SLA or quality guarantees","Artifact characterization is based on observed outputs; specific failure modes may vary with prompt content","No real-time quality feedback during generation; assessment requires post-hoc analysis","Quality metrics are not standardized; comparison with other models requires careful methodology"],"requires":["Access to generated video outputs for analysis","Understanding of video quality metrics and artifact types","Research-oriented evaluation methodology"],"input_types":["video (generated outputs for quality assessment)"],"output_types":["structured data (quality metrics, artifact descriptions, evaluation reports)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Special research access or partnership agreement with Google; not available via standard API","Text prompt describing desired video content (English language, narrative structure recommended)","Sufficient computational resources on Google's infrastructure for inference (handled server-side)","Text descriptions that explicitly name and reference entities across scenes","Narrative structure with clear scene boundaries and character/object mentions","Access to Phenaki's research API or demonstration interface","Text descriptions that clearly specify desired motion and interactions","Access to Phenaki's inference infrastructure","Patience for inference latency; motion synthesis is computationally expensive","Natural language text descriptions with clear narrative structure"],"failure_modes":["Output exhibits visible diffusion artifacts, motion inconsistencies, and characteristic blurriness typical of current generative video models","Temporal coherence degrades with narrative complexity; longer sequences show accumulated drift in object positioning and lighting","No fine-tuning or style control mechanisms exposed; outputs reflect training distribution without customization","Inference latency scales non-linearly with video length; 2+ minute generation requires significant computational resources and wall-clock time","Limited to research demonstration quality; production-grade reliability and consistency not guaranteed","Identity preservation degrades with scene count; 3+ scene narratives show measurable appearance drift","Spatial relationships become ambiguous in complex multi-character scenes; simple 1-2 character narratives perform best","No explicit control over character appearance or style; consistency emerges from training data distribution","Scene transitions may show visual discontinuities despite entity tracking; lighting and background consistency not guaranteed","Motion prediction fails for complex interactions (multiple objects colliding, fluid dynamics, cloth simulation)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"ecosystem":0.25,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.562Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=phenaki","compare_url":"https://unfragile.ai/compare?artifact=phenaki"}},"signature":"dIS+gMXF18QaYHtTw5ff9fwPA7CGipCsF0mZYJSIUkK9wL+Ll5/M6kkxVos1oT/rPEeCeN6vrmSwwXxumE+8Cw==","signedAt":"2026-06-16T09:05:19.215Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phenaki","artifact":"https://unfragile.ai/phenaki","verify":"https://unfragile.ai/api/v1/verify?slug=phenaki","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}