Image-text-to-video is generative AI's hardest unsolved frontier — animating a still image according to a text prompt while maintaining temporal coherence and physical plausibility. Stable Video Diffusion (2023) and Runway Gen-2 showed early promise, Sora (2024) raised the bar dramatically with minute-long physically consistent clips, and Kling and Veo 2 pushed quality further. The fundamental challenge is that video generation requires implicit world models: objects must persist, lighting must evolve consistently, and motion must obey approximate physics across dozens of frames. Evaluation is still largely human-judged, with FVD and CLIP-temporal scores poorly correlating with perceived quality.
Evaluates instruction-guided video generation from image+text
Leading models on VideoBench.
No results yet. Be the first to contribute.
Didn't find the model, metric, or dataset you needed? Tell us in one line. We read every message and reply within 48 hours.
Still looking for something on Image-Text-to-Video? A missing model, a stale score, a benchmark we should cover — drop it here and we'll handle it.
Real humans read every message. We track what people are asking for and prioritize accordingly.