Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
38 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation (2405.04682v4)

Published 7 May 2024 in cs.CV, cs.AI, and cs.LG
TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation

Abstract: Most of these text-to-video (T2V) generative models often produce single-scene video clips that depict an entity performing a particular action (e.g., 'a red panda climbing a tree'). However, it is pertinent to generate multi-scene videos since they are ubiquitous in the real-world (e.g., 'a red panda climbing a tree' followed by 'the red panda sleeps on the top of the tree'). To generate multi-scene videos from the pretrained T2V model, we introduce a simple and effective Time-Aligned Captions (TALC) framework. Specifically, we enhance the text-conditioning mechanism in the T2V architecture to recognize the temporal alignment between the video scenes and scene descriptions. For instance, we condition the visual features of the earlier and later scenes of the generated video with the representations of the first scene description (e.g., 'a red panda climbing a tree') and second scene description (e.g., 'the red panda sleeps on the top of the tree'), respectively. As a result, we show that the T2V model can generate multi-scene videos that adhere to the multi-scene text descriptions and be visually consistent (e.g., entity and background). Further, we finetune the pretrained T2V model with multi-scene video-text data using the TALC framework. We show that the TALC-finetuned model outperforms the baseline by achieving a relative gain of 29% in the overall score, which averages visual consistency and text adherence using human evaluation.

PDF HTML Abstract

Exploring Multi-scene Video Generation with Time-Aligned Captions (TALC)

Introduction to Multi-Scene Video Generation

In the field of text-to-video (T2V) models, recent advances have significantly improved our capability to generate detailed and visually appealing video clips from text prompts. However, these developments have predominantly focused on generating videos depicting single scenes. Real-world narratives, such as those found in movies or detailed instructions, often involve multiple scenes that smoothly transition and adhere to a coherent storyline.

This discussion explores a novel framework fittingly named Time-Aligned Captions (TALC). Unlike traditional models, TALC extends the capabilities of T2V models to not only handle more complex, multi-scene text descriptions but also ensure visual and narrative coherence throughout the video.

Challenges in Multi-Scene Development

Generating multi-scene videos offers a set of unique challenges:

  • Temporal Alignment: The video must correctly sequence events as described across different scenes in the text.
  • Visual Consistency: Characters and backgrounds must remain consistent throughout scenes unless changes are explicitly described in the text.
  • Text Adherence: Each video segment must closely align with its corresponding text, depicting the correct actions and scenarios.

Historically, models have struggled with these aspects, often either merging scenes into a continuous, somewhat jumbled depiction or losing coherence between separate scene-specific video clips.

TALC Framework Overview

TALC addresses these challenges by modifying the text-conditioning mechanisms within T2V architecture. It carefully aligns the text representation directly with corresponding segments of the video, allowing for distinctive scene transitions while maintaining overall coherence. Let's break it down:

  • Scene-Specific Conditioning: In TALC, video frames are conditioned on the embeddings of their specific scene descriptions, effectively partitioning the generative process per scene within a single coherent video output.
  • Enhanced Consistency: By integrating text descriptors through cross-attention mechanisms in a manner that respects scene boundaries, TALC helps maintain both the narrative and visual consistency across the multi-scene video.

Practical Implications and Theoretical Advancements

The introduction of TALC is a significant step forward because it allows for more complex applications of T2V technologies, including but not limited to educational content, detailed storytelling, and dynamic instruction videos.

From a theoretical standpoint, TALC enriches our understanding of multi-modal AI interactions, demonstrating a successful approach to align multi-scene narratives with visual data. This not only enhances the text-video alignment but also provides a scaffold that might be applicable in other contexts such as video summarization and more complex narrative constructions.

Speculating on Future Developments

Looking ahead, TALC opens several pathways for future research and development:

  1. Integration with Larger Models: Applying TALC to more powerful T2V models could yield even more impressive results, potentially creating videos with cinematic quality from complex scripts.
  2. Dataset Enrichment: As TALC relies on well-annotated, scene-detailed datasets, there's a potential need for dataset development that specifically caters to multi-scene video generation.
  3. Real-time Applications: Future iterations might focus on reducing computational demands, allowing TALC to be used in real-time applications, enhancing tools in video editing, virtual reality, and interactive media.

Conclusion

In essence, the Time-Aligned Captions framework significantly advances multi-scene video generation technology. By enabling more accurate and coherent video production from elaborate multi-scene texts, TALC not only enhances the current capabilities of T2V models but sets the stage for further exciting developments in the field of generative modeling.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (59)
  1. Towards high resolution video generation with progressive growing of sliced wasserstein gans. arXiv preprint arXiv:1810.02419, 2018.
  2. Generating videos with scene dynamics. Advances in neural information processing systems, 29, 2016.
  3. Genie: Generative interactive environments. arXiv preprint arXiv:2402.15391, 2024.
  4. OpenAI. Video generation models as world simulators. https://openai.com/research/video-generation-models-as-world-simulators, 2024.
  5. Video generation models as world simulators, 2024.
  6. Lumiere: A space-time diffusion model for video generation. arXiv preprint arXiv:2401.12945, 2024.
  7. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022a.
  8. Emu video: Factorizing text-to-video generation by explicit image conditioning. arXiv preprint arXiv:2311.10709, 2023.
  9. Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14125, 2023.
  10. Modelscope text-to-video technical report. arXiv preprint arXiv:2308.06571, 2023a.
  11. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563–22575, 2023a.
  12. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023b.
  13. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  14. Frozen in time: A joint video and image encoder for end-to-end retrieval. In IEEE International Conference on Computer Vision, 2021.
  15. Advancing high-resolution video-language representation with large-scale video transcriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5036–5045, 2022.
  16. Videocrafter1: Open diffusion models for high-quality video generation. arXiv preprint arXiv:2310.19512, 2023.
  17. Videocrafter2: Overcoming data limitations for high-quality video diffusion models. arXiv preprint arXiv:2401.09047, 2024.
  18. Open-sora: Democratizing efficient video production for all, March 2024. URL https://github.com/hpcaitech/Open-Sora.
  19. Videofactory: Swap attention in spatiotemporal diffusions for text-to-video generation. arXiv preprint arXiv:2305.10874, 2023b.
  20. Phenaki: Variable length video generation from open domain textual description. arXiv preprint arXiv:2210.02399, 2022.
  21. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  22. Palm 2 technical report. arXiv preprint arXiv:2305.10403, 2023.
  23. Magvit: Masked generative video transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10459–10469, 2023.
  24. Make-a-story: Visual memory conditioned consistent story generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2493–2502, 2023.
  25. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022.
  26. Storygan: A sequential conditional gan for story visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6329–6338, 2019.
  27. Nuwa-xl: Diffusion over diffusion for extremely long video generation. arXiv preprint arXiv:2303.12346, 2023.
  28. Videodirectorgpt: Consistent multi-scene video generation via llm-guided planning. arXiv preprint arXiv:2309.15091, 2023.
  29. Videodrafter: Content-consistent multi-scene video generation with llm. arXiv preprint arXiv:2401.01256, 2024.
  30. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
  31. Calvin Luo. Understanding diffusion models: A unified perspective. arXiv preprint arXiv:2208.11970, 2022.
  32. Zero-shot text-to-image generation. In International conference on machine learning, pages 8821–8831. Pmlr, 2021.
  33. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
  34. Ting Chen. On the importance of noise scheduling for diffusion models. arXiv preprint arXiv:2301.10972, 2023.
  35. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234–241. Springer, 2015.
  36. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195–4205, 2023.
  37. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  38. Hierarchical video-moment retrieval and step-captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23056–23065, 2023.
  39. Activitynet: A large-scale video benchmark for human activity understanding. In Proceedings of the ieee conference on computer vision and pattern recognition, pages 961–970, 2015.
  40. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023.
  41. Videocon: Robust video-language alignment via contrast captions. arXiv preprint arXiv:2311.10111, 2023.
  42. Msr-vtt: A large video description dataset for bridging video and language. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5288–5296, 2016.
  43. Vatex: A large-scale, high-quality multilingual dataset for video-and-language research. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4581–4591, 2019.
  44. Vbench: Comprehensive benchmark suite for video generative models. arXiv preprint arXiv:2311.17982, 2023.
  45. Storybench: A multifaceted benchmark for continuous story visualization. Advances in Neural Information Processing Systems, 36, 2024.
  46. OpenAI. Gpt-4v(ision) system card, 2023b. https://openai.com/research/gpt-4v-system-card, 2023.
  47. Videoclip: Contrastive pre-training for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021.
  48. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022.
  49. Mocogan: Decomposing motion and content for video generation. In CVPR, pages 1526–1535, 2018.
  50. Generative adversarial networks: An overview. IEEE signal processing magazine, 35(1):53–65, 2018.
  51. Carl Doersch. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908, 2016.
  52. Cogvideo: Large-scale pretraining for text-to-video generation via transformers. arXiv preprint arXiv:2205.15868, 2022.
  53. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157, 2021.
  54. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022b.
  55. Make-a-video: Text-to-video generation without text-video data. In ICLR, 2023.
  56. Dreamix: Video diffusion models are general video editors. arXiv preprint arXiv:2302.01329, 2023.
  57. Pix2video: Video editing using image diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23206–23217, 2023.
  58. Make-a-scene: Scene-based text-to-image generation with human priors. In European Conference on Computer Vision, pages 89–106. Springer, 2022.
  59. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Hritik Bansal (38 papers)
  2. Yonatan Bitton (36 papers)
  3. Michal Yarom (12 papers)
  4. Idan Szpektor (47 papers)
  5. Aditya Grover (82 papers)
  6. Kai-Wei Chang (292 papers)
Citations (3)
View on Semantic Scholar