insight - Computer Vision - # Text-Driven Synthesis of Human-Object Interactions with Multiple Objects

A Large-Scale Dataset of Full-Body Human Interacting with Multiple Household Objects

Q: How can the HIMO dataset be extended to include more diverse object types, such as large furniture or articulated objects?

To extend the HIMO dataset to include more diverse object types, such as large furniture or articulated objects, several strategies can be employed. First, the dataset can incorporate a wider range of object categories by selecting items from various environments, including living rooms, offices, and outdoor settings. This would involve identifying and 3D scanning large furniture pieces like chairs, tables, and cabinets, as well as articulated objects such as tools or toys that require complex interactions. Second, the data acquisition process would need to adapt to the unique challenges posed by larger objects. For instance, the optical MoCap system may require additional cameras or a different setup to capture the full range of motion around larger items. Furthermore, articulated objects may necessitate the use of advanced tracking techniques to accurately capture their movements and interactions with human subjects. Third, the annotation process should be expanded to include detailed descriptions of interactions with these new object types, ensuring that the fine-grained textual descriptions and temporal segments remain consistent with the existing dataset. This would enhance the dataset's utility for training models on human-object interactions involving a broader spectrum of objects, ultimately improving the generalization capabilities of the synthesized interactions.

Q: What are the potential challenges in applying the proposed methods to real-world scenarios with uncontrolled environments and occlusions?

Applying the proposed methods from the HIMO dataset to real-world scenarios presents several challenges, particularly in uncontrolled environments. One significant challenge is the variability in lighting conditions, which can affect the performance of motion capture systems and the accuracy of object tracking. In uncontrolled settings, shadows and reflections may introduce noise, complicating the extraction of precise motion data. Another challenge is the presence of occlusions, where objects or body parts may be blocked from view by other objects or individuals. The HIMO dataset employs a hybrid MoCap system to mitigate occlusions, but in real-world applications, the dynamic nature of environments can lead to unpredictable occlusions that the system may not be able to handle effectively. This could result in incomplete or inaccurate motion data, affecting the realism and plausibility of the generated human-object interactions. Additionally, the complexity of human behavior in real-world scenarios, including spontaneous actions and interactions with multiple objects simultaneously, may not be fully captured by the structured sequences in the HIMO dataset. This variability necessitates robust models capable of adapting to unforeseen circumstances and generating coherent interactions despite the lack of controlled conditions.

Q: How can the generated human-object interactions be further integrated with other modalities, such as facial expressions or dialogue, to create more realistic and engaging virtual characters?

To create more realistic and engaging virtual characters, the generated human-object interactions from the HIMO dataset can be integrated with other modalities, such as facial expressions and dialogue, through a multi-modal synthesis approach. This involves developing a framework that combines motion generation with facial animation and speech synthesis, allowing for a cohesive representation of character behavior. First, facial expressions can be modeled using a parametric facial animation system that captures the nuances of human emotions. By linking the generated human-object interactions with corresponding emotional states, the virtual character can exhibit appropriate facial expressions that reflect their actions. For instance, when a character is pouring tea, their facial expression could convey concentration or enjoyment, enhancing the realism of the interaction. Second, dialogue can be integrated by employing natural language processing (NLP) techniques to generate contextually relevant speech. The dialogue can be synchronized with the actions performed by the character, ensuring that the spoken words align with the ongoing interactions. For example, while pouring tea, the character might say, "Let me pour you a cup," which adds depth to the interaction and engages the audience. Finally, a unified framework that combines these modalities can utilize temporal alignment techniques to ensure that the timing of facial expressions, speech, and body movements are coherent. This holistic approach not only enhances the believability of virtual characters but also allows for more dynamic storytelling and interaction in applications such as video games, virtual reality, and animated films. By leveraging the rich data from the HIMO dataset and integrating it with facial and dialogue modalities, developers can create immersive experiences that resonate with users.

Conceitos essenciais

This paper introduces HIMO, a large-scale dataset of full-body human interacting with multiple household objects, enabling the study of text-driven synthesis of complex human-object interactions.

Resumo

The authors present HIMO, a large-scale dataset for the study of full-body human interacting with multiple household objects. The dataset contains 3.3K 4D human-object interaction (HOI) sequences with 4.08M 3D HOI frames, captured using a hybrid motion capture system.

The key highlights of the HIMO dataset are:

It includes full-body human motion, object motions, and mutual contacts, enabling the study of complex HOIs involving multiple objects.
The dataset is annotated with detailed textual descriptions for each HOI sequence, as well as temporal segmentation labels, facilitating two novel tasks:
a) Text-driven HOI synthesis conditioned on the whole text prompt (HIMO-Gen)
b) Text-driven HOI synthesis conditioned on segmented text prompts for fine-grained timeline control (HIMO-SegGen)
The authors propose a dual-branch conditional diffusion model with a mutual interaction module to address the HIMO-Gen task, ensuring coordinated generation of human and object motions.
For HIMO-SegGen, an auto-regressive generation pipeline is introduced to obtain smooth transitions between the generated HOI segments.
Experiments demonstrate the generalization ability of the proposed methods to unseen object geometries and novel HOI compositions.

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Estatísticas

"Humans constantly interact with objects as daily routines."
"Most of the previous datasets and models are limited to interacting with a single object, yet neglect the ubiquitous functionality combination of multiple objects."
"We adopt the optical MoCap system to obtain precise body movements and track the motion of objects attached by reflective markers."
"In total, 3.3K 4D HOI sequences with 34 subjects performing the combinations of 53 daily objects are presented, resulting in 4.08M 3D HOI frames."

Citações

"Generating human-object interactions (HOIs) is critical with the tremendous advances of digital avatars."
"Intuitively, the multiple objects setting is more practical and allows for broader applications, such as manipulating multiple objects for robotics."
"To facilitate the study of text-driven HOI synthesis, we annotate the HOI sequences with fine-grained textual descriptions."

Principais Insights Extraídos De

HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects

by Xintao Lv, L... às arxiv.org 09-12-2024

https://arxiv.org/pdf/2407.12371.pdf

HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects

Perguntas Mais Profundas

How can the HIMO dataset be extended to include more diverse object types, such as large furniture or articulated objects?

To extend the HIMO dataset to include more diverse object types, such as large furniture or articulated objects, several strategies can be employed. First, the dataset can incorporate a wider range of object categories by selecting items from various environments, including living rooms, offices, and outdoor settings. This would involve identifying and 3D scanning large furniture pieces like chairs, tables, and cabinets, as well as articulated objects such as tools or toys that require complex interactions.
Second, the data acquisition process would need to adapt to the unique challenges posed by larger objects. For instance, the optical MoCap system may require additional cameras or a different setup to capture the full range of motion around larger items. Furthermore, articulated objects may necessitate the use of advanced tracking techniques to accurately capture their movements and interactions with human subjects.
Third, the annotation process should be expanded to include detailed descriptions of interactions with these new object types, ensuring that the fine-grained textual descriptions and temporal segments remain consistent with the existing dataset. This would enhance the dataset's utility for training models on human-object interactions involving a broader spectrum of objects, ultimately improving the generalization capabilities of the synthesized interactions.

What are the potential challenges in applying the proposed methods to real-world scenarios with uncontrolled environments and occlusions?

Applying the proposed methods from the HIMO dataset to real-world scenarios presents several challenges, particularly in uncontrolled environments. One significant challenge is the variability in lighting conditions, which can affect the performance of motion capture systems and the accuracy of object tracking. In uncontrolled settings, shadows and reflections may introduce noise, complicating the extraction of precise motion data.
Another challenge is the presence of occlusions, where objects or body parts may be blocked from view by other objects or individuals. The HIMO dataset employs a hybrid MoCap system to mitigate occlusions, but in real-world applications, the dynamic nature of environments can lead to unpredictable occlusions that the system may not be able to handle effectively. This could result in incomplete or inaccurate motion data, affecting the realism and plausibility of the generated human-object interactions.
Additionally, the complexity of human behavior in real-world scenarios, including spontaneous actions and interactions with multiple objects simultaneously, may not be fully captured by the structured sequences in the HIMO dataset. This variability necessitates robust models capable of adapting to unforeseen circumstances and generating coherent interactions despite the lack of controlled conditions.

How can the generated human-object interactions be further integrated with other modalities, such as facial expressions or dialogue, to create more realistic and engaging virtual characters?

To create more realistic and engaging virtual characters, the generated human-object interactions from the HIMO dataset can be integrated with other modalities, such as facial expressions and dialogue, through a multi-modal synthesis approach. This involves developing a framework that combines motion generation with facial animation and speech synthesis, allowing for a cohesive representation of character behavior.
First, facial expressions can be modeled using a parametric facial animation system that captures the nuances of human emotions. By linking the generated human-object interactions with corresponding emotional states, the virtual character can exhibit appropriate facial expressions that reflect their actions. For instance, when a character is pouring tea, their facial expression could convey concentration or enjoyment, enhancing the realism of the interaction.
Second, dialogue can be integrated by employing natural language processing (NLP) techniques to generate contextually relevant speech. The dialogue can be synchronized with the actions performed by the character, ensuring that the spoken words align with the ongoing interactions. For example, while pouring tea, the character might say, "Let me pour you a cup," which adds depth to the interaction and engages the audience.
Finally, a unified framework that combines these modalities can utilize temporal alignment techniques to ensure that the timing of facial expressions, speech, and body movements are coherent. This holistic approach not only enhances the believability of virtual characters but also allows for more dynamic storytelling and interaction in applications such as video games, virtual reality, and animated films. By leveraging the rich data from the HIMO dataset and integrating it with facial and dialogue modalities, developers can create immersive experiences that resonate with users.