içgörü - Human motion estimation - # Scene-aware Full-Body Motion Generation from Sparse Signals

Estimating Full-Body Human Motion in 3D Scenes from Sparse Tracking Signals using a Unified Diffusion Framework

Q: How can S2Fusion be extended to handle more complex human-object interactions in 3D scenes

To extend S2Fusion for more complex human-object interactions in 3D scenes, several enhancements can be considered. One approach is to incorporate additional sensors or modalities to capture more detailed information about the interactions. For example, depth sensors or RGB-D cameras can provide depth information that can help in understanding the spatial relationships between humans and objects in the scene. By integrating this depth information into the model, S2Fusion can better estimate the interactions and generate more accurate and realistic motions. Another extension could involve incorporating semantic information about the objects in the scene. By leveraging object recognition algorithms or semantic segmentation techniques, S2Fusion can identify different objects in the environment and adjust the generated motions accordingly. For instance, if a person is interacting with a chair, the model can adapt the motion to simulate sitting or standing up from the chair. Furthermore, integrating physics-based simulations or constraints into the model can enhance the realism of the generated motions. By incorporating knowledge of physical interactions, such as gravity, friction, or collision detection, S2Fusion can generate more physically plausible movements in complex human-object interactions.

Q: What other modalities, beyond scene geometry and sparse tracking signals, could be leveraged to further improve the quality and diversity of the generated full-body motions

Beyond scene geometry and sparse tracking signals, S2Fusion can leverage additional modalities to further enhance the quality and diversity of the generated full-body motions. One potential modality is audio data, which can provide cues about the context or intention of the movements. By incorporating audio signals into the model, S2Fusion can generate motions that are synchronized with speech or sound cues, adding another layer of realism to the animations. Another modality that can be leveraged is contextual information from the environment. This could include factors such as lighting conditions, temperature, or even emotional cues from the surroundings. By integrating these contextual cues into the model, S2Fusion can generate motions that are more contextually aware and responsive to the environment. Additionally, incorporating feedback mechanisms or reinforcement learning techniques can enable S2Fusion to adapt and improve its motion generation based on user interactions or preferences. By learning from user feedback, the model can continuously refine and optimize the generated motions to better meet the user's expectations and requirements.

Q: Can the periodic motion feature extraction technique used in S2Fusion be applied to other motion-related tasks, such as motion style transfer or motion retargeting

The periodic motion feature extraction technique used in S2Fusion can indeed be applied to other motion-related tasks, such as motion style transfer or motion retargeting. In motion style transfer, the periodic features can capture the underlying movement patterns and styles of different motions, allowing for the transfer of these styles to new motion sequences. By extracting and manipulating these periodic features, S2Fusion can facilitate the transfer of specific movement characteristics or styles from one motion to another. Similarly, in motion retargeting, the periodic motion features can aid in aligning and adapting motions from one character or skeleton to another. By analyzing the temporal alignment and spatial relationships encoded in the periodic features, S2Fusion can help in retargeting motions to different characters or skeletons while preserving the original motion's style and dynamics. This can be particularly useful in applications such as animation production or virtual character control, where motion retargeting is essential for creating diverse and realistic animations.

Temel Kavramlar

A unified diffusion framework, S2Fusion, that combines scene geometry and sparse tracking signals to generate plausible and coherent full-body human motions, overcoming the inherent ambiguities in the sparse-to-dense mapping problem.

Özet

The paper introduces a new framework, S2Fusion, that combines scene information and sparse tracking signals to estimate full-body human motion in 3D scenes. The key highlights are:

S2Fusion models the task of estimating full-body motion from sparse tracking signals and scene geometry using a conditional diffusion framework. This helps resolve the inherent ambiguities in the sparse-to-dense mapping problem.
To handle the limited data volume in motion-scene datasets and generate more diverse motions, S2Fusion initializes the diffusion process with a pre-trained motion prior.
S2Fusion extracts periodic motion features from the sparse tracking signals using a periodic autoencoder. This helps capture the spatial-temporal alignment of the full-body motions and improves the coordination between the upper and lower body.
To regularize the lower body motion in the absence of tracking signals, S2Fusion incorporates two specially designed loss functions - scene-penetration loss and phase-matching loss - in the loss-guided diffusion sampling process. This ensures the generated motions are physically plausible and coherent.
Extensive experiments on two motion-scene datasets demonstrate that S2Fusion outperforms state-of-the-art methods in terms of motion estimation accuracy, smoothness, and scene awareness.

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İstatistikler

The average per joint rotation error (MPJRE) of our method is 4.65 on GIMO and 2.32 on CIRCLE, outperforming other methods.
The average per joint position error (MPJPE) of our method is 57.8 on GIMO and 19.2 on CIRCLE, significantly better than other methods.
The average per joint velocity error (MPJVE) of our method is 235.7 on GIMO and 117.6 on CIRCLE, indicating smoother motions.
The foot sliding metric (FS) of our method is 1.39 on GIMO and 1.48 on CIRCLE, showing better scene awareness.

Alıntılar

"To estimate plausible human motions given sparse tracking signals and 3D scenes, we develop S2Fusion, a unified framework fusing Scene and sparse Signals with a conditional difFusion model."
"To tackle the lack of paired motion-scene datasets and to generate more diverse motion, the reverse diffusion process starts from a non-Gaussian motion distribution, by adopting a pre-trained motion prior on a large-scale motion dataset [40]."
"To facilitate the mitigating of unrealistic lower body motions, we guide the diffusion sampling process by the gradient of our specially designed loss functions, under the framework of loss-guided sampling [46]."

Önemli Bilgiler Şuradan Elde Edildi

A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals

by Jiangnan Tan... : arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04890.pdf

A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals

Daha Derin Sorular

How can S2Fusion be extended to handle more complex human-object interactions in 3D scenes

To extend S2Fusion for more complex human-object interactions in 3D scenes, several enhancements can be considered. One approach is to incorporate additional sensors or modalities to capture more detailed information about the interactions. For example, depth sensors or RGB-D cameras can provide depth information that can help in understanding the spatial relationships between humans and objects in the scene. By integrating this depth information into the model, S2Fusion can better estimate the interactions and generate more accurate and realistic motions.
Another extension could involve incorporating semantic information about the objects in the scene. By leveraging object recognition algorithms or semantic segmentation techniques, S2Fusion can identify different objects in the environment and adjust the generated motions accordingly. For instance, if a person is interacting with a chair, the model can adapt the motion to simulate sitting or standing up from the chair.
Furthermore, integrating physics-based simulations or constraints into the model can enhance the realism of the generated motions. By incorporating knowledge of physical interactions, such as gravity, friction, or collision detection, S2Fusion can generate more physically plausible movements in complex human-object interactions.

What other modalities, beyond scene geometry and sparse tracking signals, could be leveraged to further improve the quality and diversity of the generated full-body motions

Beyond scene geometry and sparse tracking signals, S2Fusion can leverage additional modalities to further enhance the quality and diversity of the generated full-body motions. One potential modality is audio data, which can provide cues about the context or intention of the movements. By incorporating audio signals into the model, S2Fusion can generate motions that are synchronized with speech or sound cues, adding another layer of realism to the animations.
Another modality that can be leveraged is contextual information from the environment. This could include factors such as lighting conditions, temperature, or even emotional cues from the surroundings. By integrating these contextual cues into the model, S2Fusion can generate motions that are more contextually aware and responsive to the environment.
Additionally, incorporating feedback mechanisms or reinforcement learning techniques can enable S2Fusion to adapt and improve its motion generation based on user interactions or preferences. By learning from user feedback, the model can continuously refine and optimize the generated motions to better meet the user's expectations and requirements.

Can the periodic motion feature extraction technique used in S2Fusion be applied to other motion-related tasks, such as motion style transfer or motion retargeting

The periodic motion feature extraction technique used in S2Fusion can indeed be applied to other motion-related tasks, such as motion style transfer or motion retargeting. In motion style transfer, the periodic features can capture the underlying movement patterns and styles of different motions, allowing for the transfer of these styles to new motion sequences. By extracting and manipulating these periodic features, S2Fusion can facilitate the transfer of specific movement characteristics or styles from one motion to another.
Similarly, in motion retargeting, the periodic motion features can aid in aligning and adapting motions from one character or skeleton to another. By analyzing the temporal alignment and spatial relationships encoded in the periodic features, S2Fusion can help in retargeting motions to different characters or skeletons while preserving the original motion's style and dynamics. This can be particularly useful in applications such as animation production or virtual character control, where motion retargeting is essential for creating diverse and realistic animations.