Realistic Human Dance Generation with Disentangled Control and Generalizability

Core Concepts

DISCO, a novel approach for realistic human dance generation in social media scenarios, enables faithful and flexible synthesis by disentangling the control of human subject, background, and pose, while also improving generalizability to unseen humans, backgrounds, and poses through effective human attribute pre-training.

Abstract

The paper introduces DISCO, a novel approach for realistic human dance generation in social media scenarios. It highlights two key properties that are missing from conventional human motion transfer methods: generalizability and compositionality.
To address these challenges, DISCO consists of two key designs:

A novel model architecture with disentangled control: DISCO disentangles the control of human foreground, background, and pose, enabling arbitrary compositionality of these elements from different sources. This is achieved by utilizing a VAE as the background encoder, a convolutional encoder for the pose, and incorporating the CLIP image embedding with the denoising U-Net for the human subject.
An effective human attribute pre-training: To improve generalizability to unseen humans, DISCO is pre-trained on a large-scale collection of human images, where the model learns to reconstruct the complete image given the separate foreground and background features. This enables the model to effectively distinguish the dynamic human subject from the static background and better encode diverse human attributes.

Extensive qualitative and quantitative evaluations demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions, outperforming state-of-the-art methods. DISCO also exhibits strong zero-shot generalization to unseen human subjects, backgrounds, and poses, as well as different datasets and real-world YouTube videos.

Stats

DISCO can generate high-quality human dance images and videos with FID of 28.31 and FID-VID of 55.17, outperforming state-of-the-art methods.
Adding temporal modeling further boosts DISCO's FID-VID score to 29.37.

Quotes

"DISCO can not only enable arbitrary compositionality of human subjects, backgrounds, and dance-moves, but also achieve high fidelity via the thorough utilization of the various input conditions."
"Without the constraint of pairwise human images for pose control, we can overcome the insufficiency of high-quality dance video data by leveraging large-scale collections of human images to learn diverse human attributes, in turn, greatly improve the generalizability of DISCO to unseen humans."

Key Insights Distilled From

DisCo

by Tan Wang,Lin... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2307.00040.pdf

Deeper Inquiries

How can DISCO's disentangled control and generalizability be extended to handle more complex human-object interactions or multi-person scenarios

To extend DISCO's disentangled control and generalizability to handle more complex human-object interactions or multi-person scenarios, several modifications and enhancements can be considered:

Multi-Object Interaction: Introducing additional control branches or modules dedicated to different objects or entities in the scene can help DISCO manage interactions between humans and objects. By disentangling the controls for each element, the model can better understand and manipulate complex interactions.

Hierarchical Control: Implementing a hierarchical control mechanism can enable DISCO to handle multi-person scenarios more effectively. By hierarchically organizing the controls for different individuals or objects in the scene, the model can generate coherent and realistic interactions between them.

Attention Mechanisms: Incorporating attention mechanisms that dynamically focus on different parts of the scene can enhance DISCO's ability to capture intricate interactions. By attending to relevant regions based on the context, the model can generate more realistic and contextually appropriate outputs.

Fine-Grained Attribute Control: Enhancing the granularity of attribute control can help DISCO capture subtle nuances in human-object interactions. By allowing for precise adjustments to attributes like pose, appearance, and behavior, the model can generate more detailed and accurate outputs.

By incorporating these enhancements, DISCO can be adapted to handle a wider range of complex scenarios involving human-object interactions and multi-person dynamics.

What are the potential limitations of the human attribute pre-training approach, and how could it be further improved to better capture the nuances of human appearance and motion

While human attribute pre-training offers significant benefits in improving generalizability and capturing diverse human attributes, there are potential limitations and areas for improvement:

Limited Dataset Representativeness: The effectiveness of human attribute pre-training heavily relies on the diversity and representativeness of the pre-training dataset. To address this limitation, expanding the dataset to include a broader range of human attributes, poses, and appearances can enhance the model's ability to capture nuanced variations.

Fine-Grained Attribute Encoding: Enhancing the encoding mechanisms for fine-grained attributes, such as facial expressions, clothing details, and body movements, can improve the model's ability to capture subtle variations in human appearance and motion. Utilizing advanced encoding techniques like attention mechanisms or transformer architectures can help capture these nuances more effectively.

Dynamic Attribute Adaptation: Implementing mechanisms for dynamic attribute adaptation during training and inference can enable the model to adjust attributes based on contextual cues or user inputs. By allowing for real-time attribute modifications, the model can generate more personalized and contextually relevant outputs.

Continual Learning: Incorporating continual learning strategies to adapt the model to new attributes or variations over time can ensure that the model remains up-to-date and capable of handling evolving human attributes and behaviors.

By addressing these limitations and implementing improvements in dataset diversity, attribute encoding, dynamic adaptation, and continual learning, the human attribute pre-training approach can be further enhanced to better capture the nuances of human appearance and motion.

Given the impressive results on human dance generation, how could DISCO's core ideas be adapted to enable more general video editing capabilities, such as seamless object insertion, removal, or transformation

To adapt DISCO's core ideas for more general video editing capabilities, such as seamless object insertion, removal, or transformation, the following strategies can be considered:

Object Segmentation and Manipulation: Introducing object segmentation modules that can identify and isolate specific objects in the scene can enable DISCO to manipulate objects independently. By incorporating object-aware controls and transformations, the model can seamlessly insert, remove, or transform objects in the video.

Object Interaction Modeling: Implementing mechanisms to model interactions between objects and the environment can enhance DISCO's ability to generate realistic object behaviors. By simulating physics-based interactions or object-object interactions, the model can generate more dynamic and realistic video edits.

Contextual Understanding: Enhancing DISCO's contextual understanding capabilities through advanced attention mechanisms or contextual embeddings can improve the model's ability to generate coherent and contextually relevant video edits. By considering the broader context of the scene, including object relationships and spatial arrangements, the model can produce more visually appealing and coherent results.

Fine-Grained Control: Providing users with fine-grained control over object manipulation, including attributes like position, scale, orientation, and appearance, can enable precise and detailed video editing. By allowing users to interactively adjust object properties, DISCO can facilitate intuitive and customizable video editing experiences.

By incorporating these strategies and adapting DISCO's core ideas for object manipulation and transformation, the model can be extended to offer more general video editing capabilities, catering to a wider range of editing tasks beyond human dance generation.