# TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models

arXiv: https://arxiv.org/abs/2404.16306 · HTML: https://mlsanity.com/paper/2404.16306/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 64 of its 72 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 64)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)

## Built on by (most cited first, 25 of 25)

- [Exploring the Evolution of Physics Cognition in Video Generation: A Survey](https://mlsanity.com/paper/2503.21765/) (2025)
- [FAVOR-Bench: A Comprehensive Benchmark for Fine-Grained Video Motion Understanding](https://mlsanity.com/paper/2503.14935/) (2025)
- [GeoVideo: Introducing Geometric Regularization into Video Generation Model](https://mlsanity.com/paper/2512.03453/) (2025)
- [Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach](https://mlsanity.com/paper/2410.03160/) (2024)
- [MotiF: Making Text Count in Image Animation with Motion Focal Loss](https://mlsanity.com/paper/2412.16153/) (2024)
- [Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation](https://mlsanity.com/paper/2512.23705/) (2025)
- [RISE-Video: Can Video Generators Decode Implicit World Rules?](https://mlsanity.com/paper/2602.05986/) (2026)
- [PoseTraj: Pose-Aware Trajectory Control in Video Diffusion](https://mlsanity.com/paper/2503.16068/) (2025)
- [DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation](https://mlsanity.com/paper/2503.21246/) (2025)
- [CtrlAttack: A Unified Attack on World-Model Control in Diffusion Models](https://mlsanity.com/paper/2603.13435/) (2026)
- [Chain of Event-Centric Causal Thought for Physically Plausible Video Generation](https://mlsanity.com/paper/2603.09094/) (2026)
- [HANDI: Hand-Centric Text-and-Image Conditioned Video Generation](https://mlsanity.com/paper/2412.04189/) (2024)
- [Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation](https://mlsanity.com/paper/2507.16116/) (2025)
- [CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping](https://mlsanity.com/paper/2605.30774/) (2026)
- [Image-to-Video Diffusion: From Foundations to Open Frontiers](https://mlsanity.com/paper/2605.17248/) (2026)
- [Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation](https://mlsanity.com/paper/2609.07720/) (2026)
- [Measuring 3D Spatial Geometric Consistency in Dynamic Video Generation](https://mlsanity.com/paper/2603.19048/) (2026)
- [Physics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation](https://mlsanity.com/paper/2606.25306/) (2026)
- [Learning to Animate Images from A Few Videos to Portray Delicate Human Actions](https://mlsanity.com/paper/2503.00276/) (2025)
- [Noise Consistency Regularization for Improved Subject-Driven Image Synthesis](https://mlsanity.com/paper/2506.06483/) (2025)
- [Anchored Video Generation: Decoupling Scene Construction and Temporal Synthesis in Text-to-Video Diffusion Models](https://mlsanity.com/paper/2512.16371/) (2025)
- [Programmatic Video Prediction Using Large Language Models](https://mlsanity.com/paper/2505.14948/) (2025)
- [Unified Text-Image-to-Video Generation: A Training-Free Approach to Flexible Visual Conditioning](https://mlsanity.com/paper/2505.20629/) (2025)
- [Good Noise Makes Good Edits: A Training-Free Diffusion-Based Video Editing with Image and Text Prompts](https://mlsanity.com/paper/2506.12520/) (2025)
- [Physically Plausible Video Generation via Visual-Semantic Chain-of-Events Conditioning](https://mlsanity.com/paper/2609.00656/) (2026)

## Abstract (arXiv)

Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., "a woman is drinking water."). Existing TI2V frameworks often require costly training on video-text datasets and specific model designs for text and image conditioning. In this paper, we propose TI2V-Zero, a zero-shot, tuning-free method that empowers a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image, enabling TI2V generation without any optimization, fine-tuning, or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the additional image input, we propose a "repeat-and-slide" strategy that modulates the reverse denoising process, allowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image. To ensure temporal continuity, we employ a DDPM inversion strategy to initialize Gaussian noise for each newly synthesized frame and a resampling technique to help preserve visual details. We conduct comprehensive experiments on both domain-specific and open-domain datasets, where TI2V-Zero consistently outperforms a recent open-domain TI2V model. Furthermore, we show that TI2V-Zero can seamlessly extend to other tasks such as video infilling and prediction when provided with more images. Its autoregressive design also supports long video generation.