TIV-Diffusion: Towards Object-Centric Movement for Text-driven Image to Video Generation (2412.10275v2)

Abstract: Text-driven Image to Video Generation (TI2V) aims to generate controllable video given the first frame and corresponding textual description. The primary challenges of this task lie in two parts: (i) how to identify the target objects and ensure the consistency between the movement trajectory and the textual description. (ii) how to improve the subjective quality of generated videos. To tackle the above challenges, we propose a new diffusion-based TI2V framework, termed TIV-Diffusion, via object-centric textual-visual alignment, intending to achieve precise control and high-quality video generation based on textual-described motion for different objects. Concretely, we enable our TIV-Diffuion model to perceive the textual-described objects and their motion trajectory by incorporating the fused textual and visual knowledge through scale-offset modulation. Moreover, to mitigate the problems of object disappearance and misaligned objects and motion, we introduce an object-centric textual-visual alignment module, which reduces the risk of misaligned objects/motion by decoupling the objects in the reference image and aligning textual features with each object individually. Based on the above innovations, our TIV-Diffusion achieves state-of-the-art high-quality video generation compared with existing TI2V methods.

• The paper introduces a diffusion-based framework that employs object-centric textual alignment to precisely control object movements in video generation.
• It integrates scale-offset modulation via a SPADE-like approach to fuse appearance and motion cues, enhancing visual quality.
• The paper utilizes adaptive slot selection with Gumbel-Softmax to prevent object deformation and maintain consistent object integrity across frames.

Insights on "TIV-Diffusion: Towards Object-Centric Movement for Text-driven Image to Video Generation"

In this paper, the authors introduce a novel framework, TIV-Diffusion, designed for the task of Text-driven Image to Video Generation (TI2V). The primary challenges addressed by this framework include ensuring the accurate alignment of object movements with textual descriptions and enhancing the visual quality of the generated videos. These challenges are critical given the growing demand for controllable video generation using artificial intelligence, which has numerous applications in areas like creative content creation and augmented data generation.

Methodological Contributions

The authors propose a diffusion-based approach, leveraging object-centric textual-visual alignment to improve the generation process. Key innovations in TIV-Diffusion include:

1. Object-Centric Textual-Visual Alignment: To overcome the inherent ambiguity in textual descriptions and ensure precise object movements, the authors introduce a module that disentangles objects within an image and aligns each object individually with the textual description. This approach prevents misalignment, ensuring that the generated video frames maintain semantic consistency with the text input.
2. Scale-Offset Modulation: The fusion of textual and visual information is achieved using a SPADE-like modulation technique, which integrates appearance and motion information directly into the video generation process. By modulating the feature maps, TIV-Diffusion enhances the control over object movements specified by the textual descriptions.
3. Adaptive Slot Selection with Gumbel-Softmax: The text-enhanced object slots are incorporated into the diffusion process, allowing the model to adaptively focus on the relevant object attributes during video generation. This adaptive slot selection helps mitigate issues of object deformation and disappearance across frames.

Experimental Results

The TIV-Diffusion framework demonstrates state-of-the-art performance on existing TI2V datasets, including synthetic benchmarks like MNIST and CATER, as well as real-world scenarios. Quantitative evaluations show improvements in metrics like SSIM, PSNR, LPIPS, FID, and FVD, reflecting both the perceptual quality and the degree of control achieved over generated video content.

The authors also exemplify the effectiveness of their approach through extensive qualitative analyses. They demonstrate the model's ability to precisely execute textual commands, handle multiple objects, and maintain object integrity, even during complex interactions like occlusions and overlaps.

Implications and Future Research

The introduction of TIV-Diffusion represents a significant step forward in controllable video generation. The framework’s ability to disentangle objects and align them with textual inputs paves the way for more sophisticated generative models capable of producing high-quality, semantically consistent videos.

Future developments may explore extending the model's capabilities to accommodate more complex scene dynamics and interactions, potentially integrating additional modalities such as audio. Moreover, the authors suggest that their approach could serve as a foundation for broader applications, including video editing and interactive content creation. Further research could also focus on enhancing model efficiency to reduce computational requirements during training and inference.

Replacing traditional generative approaches with advanced diffusion models, TIV-Diffusion underscores the potential for object-centric designs in narrowing the gap between generated media and human-like comprehension in dynamic environments.