# TVG: A Training-free Transition Video Generation Method with Diffusion Models

arXiv: https://arxiv.org/abs/2408.13413 · HTML: https://mlsanity.com/paper/2408.13413/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 38 of its 59 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Placed in

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

## Builds on (most cited first, 30 of 38)

- [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)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets](https://mlsanity.com/paper/2202.00273/) (2022)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [ControlVideo: Training-free Controllable Text-to-Video Generation](https://mlsanity.com/paper/2305.13077/) (2023)
- [StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation](https://mlsanity.com/paper/2405.01434/) (2024)
- [SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction](https://mlsanity.com/paper/2310.20700/) (2023)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [FreeU: Free Lunch in Diffusion U-Net](https://mlsanity.com/paper/2309.11497/) (2023)
- [Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation](https://mlsanity.com/paper/2304.08477/) (2023)
- [MotionClone: Training-Free Motion Cloning for Controllable Video Generation](https://mlsanity.com/paper/2406.05338/) (2024)
- [FreeInit: Bridging Initialization Gap in Video Diffusion Models](https://mlsanity.com/paper/2312.07537/) (2023)
- [FiT: Flexible Vision Transformer for Diffusion Model](https://mlsanity.com/paper/2402.12376/) (2024)
- [Fast Vision Transformers with HiLo Attention](https://mlsanity.com/paper/2205.13213/) (2022)
- [Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing](https://mlsanity.com/paper/2309.15664/) (2023)
- [ToonCrafter: Generative Cartoon Interpolation](https://mlsanity.com/paper/2405.17933/) (2024)
- [DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing](https://mlsanity.com/paper/2312.07409/) (2023)
- [Towards Extreme Image Compression with Latent Feature Guidance and Diffusion Prior](https://mlsanity.com/paper/2404.18820/) (2024)
- [Mora: Enabling Generalist Video Generation via A Multi-Agent Framework](https://mlsanity.com/paper/2403.13248/) (2024)
- [Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2405.00571/) (2024)

## Built on by (most cited first, 15 of 15)

- [StableV2V: Stablizing Shape Consistency in Video-to-Video Editing](https://mlsanity.com/paper/2411.11045/) (2024)
- [PERSE: Personalized 3D Generative Avatars from A Single Portrait](https://mlsanity.com/paper/2412.21206/) (2024)
- [LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation](https://mlsanity.com/paper/2508.03485/) (2025)
- [Versatile Transition Generation with Image-to-Video Diffusion](https://mlsanity.com/paper/2508.01698/) (2025)
- [DiTalker: A Unified DiT-based Framework for High-Quality and Speaking Styles Controllable Portrait Animation](https://mlsanity.com/paper/2508.06511/) (2025)
- [Image-to-Video Diffusion: From Foundations to Open Frontiers](https://mlsanity.com/paper/2605.17248/) (2026)
- [SAGE: Structure-Aware Generative Video Transitions between Diverse Clips](https://mlsanity.com/paper/2510.24667/) (2025)
- [ESA: Energy-Based Shot Assembly Optimization for Automatic Video Editing](https://mlsanity.com/paper/2511.02505/) (2025)
- [Which Way from B to A: The role of embedding geometry in image interpolation for Stable Diffusion](https://mlsanity.com/paper/2511.12757/) (2025)
- [GeoMVD: Geometry-Enhanced Multi-View Generation Model Based on Geometric Information Extraction](https://mlsanity.com/paper/2511.12204/) (2025)
- [VC-Bench: Pioneering the Video Connecting Benchmark with a Dataset and Evaluation Metrics](https://mlsanity.com/paper/2601.19236/) (2026)
- [Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos](https://mlsanity.com/paper/2604.17749/) (2026)
- [Inverting the Streaming-Diffusion Bottleneck: Video-Rate MLLM-Conditioned Edit Diffusion on a Consumer GPU](https://mlsanity.com/paper/2606.05981/) (2026)
- [PhysFlow: Physics-Aware Optical Flow for Motion Controllable Video Generation](https://mlsanity.com/paper/2609.08215/) (2026)
- [No Corners Cut: State-Grounded Transitions for Mid-Stream Prompt Switches in Video Generation](https://mlsanity.com/paper/2609.38691/) (2026)

## Abstract (arXiv)

Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Traditional methods like morphing often lack artistic appeal and require specialized skills, limiting their effectiveness. Recent advances in diffusion model-based video generation offer new possibilities for creating transitions but face challenges such as poor inter-frame relationship modeling and abrupt content changes. We propose a novel training-free Transition Video Generation (TVG) approach using video-level diffusion models that addresses these limitations without additional training. Our method leverages Gaussian Process Regression (GPR) to model latent representations, ensuring smooth and dynamic transitions between frames. Additionally, we introduce interpolation-based conditional controls and a Frequency-aware Bidirectional Fusion (FBiF) architecture to enhance temporal control and transition reliability. Evaluations of benchmark datasets and custom image pairs demonstrate the effectiveness of our approach in generating high-quality smooth transition videos. The code are provided in https://sobeymil.github.io/tvg.com.