Advancements in Human-Object Interaction Modeling

Interactions between people and objects are influenced by not just how the objects look and where they are, but also by their physical traits like how heavy they are and how much friction they have. These traits add important details to how people move, making animations look more real. While there have been improvements in methods that focus on movement, this area has often been overlooked.

Creating smooth and realistic human movement comes with two main challenges. First, it's not easy to learn from the many types of information that involve both human movements and object details. This includes both physical properties and other non-physical attributes. Second, there isn't a good dataset that captures a variety of human interactions with objects that have different physical qualities. This lack of data makes it hard to create better models.

To tackle this issue, a new model called FORCE was created. This model focuses on how physical properties affect human interactions with objects, allowing for a wider range of realistic movements. The main idea is that human movement is affected by the amount of force a person applies and how much Resistance the object provides. By using a new method of encoding intuitive physics, this model captures how human force and object resistance work together. Tests showed that including human force helps learn different types of motion.

Along with the model, a new dataset called FORCE dataset was introduced. This dataset contains various movements that occur when interacting with objects that have different levels of resistance. With this new dataset and model, researchers hope to encourage further studies in this area.

#Challenges in Human-Object Interaction

Creating realistic movements for human-object interactions is a tough task. The challenge lies in the complex ways humans and objects interact. Previous works have mostly focused on basic aspects of interactions, like object shape and position, but they have missed out on important physical traits like weight and friction. These details are vital for making actions like carrying an empty suitcase versus a full one distinct. If the interaction is not possible, the model needs to know that; otherwise, it lacks realism. This work aims to fill that gap by considering physical traits to create lifelike human movements in various situations.

Physics-based methods combined with reinforcement learning have shown good results when dealing with various external Forces. However, they face challenges like high complexity, as they often need special training with tailored reward systems for different tasks. Because of this, a mixed approach is usually needed. Moreover, these methods can struggle when it comes to providing fine control, such as switching from using one hand to two hands.

On the other hand, kinematics-based methods for creating human Motions are easier to scale. This quality is important for applications like augmented and virtual reality, where a single model can be used for complex interactions over time. However, older kinematic methods often ignore the surrounding environment or focus only on still objects. The closest approaches used object shapes but overlooked the physical traits of interactions. In reality, humans adjust their movements based on how much resistance they feel and the force they apply when dealing with an object.

For instance, when pushing a heavy object, a person exerts greater force and changes their posture, leaning forward to cope with friction. If the resistance is too high, the object won’t budge, and the person will stop trying to interact with it. This kind of nuanced motion requires a method that can adapt to the physical features of the interaction.

#Introducing the FORCE Model

Creating a kinematics method to synthesize these interactions presents multiple challenges. First, it's tough to reason about the many types of information that come from humans and objects, like different actions, shapes of objects, and important physical traits. The complexity here complicates attempts to tell apart similar human movements, resulting in actions that lack detail and variety. Second, determining if an interaction can happen includes more than just resistance. It also depends on how the human interacts with the object. For example, a person can handle a heavier object better with both hands than with just one. It has been shown that merely focusing on resistance leads to less optimal results.

Another problem is that there isn't a dataset available that captures various daily interactions under different physical conditions. This lack of data makes it hard to build and assess models. Collecting such data can also be difficult due to issues like objects being blocked from view.

To counter these challenges, the FORCE model was developed. This is the first method that focuses on the intricate details of human-object interactions while modeling physical traits like resistance and applied human force. The model operates on a crucial insight: human motion is governed by the relationship between the force a person applies and the resistance they sense. With a new intuitive physics encoding based on these important traits, the model can create a wide range of interactions. For instance, the model can produce various motions for a "carrying" scenario, including carrying an object, needing to drop it, or realizing that carrying it is just not possible. Besides that, it allows for control during runtime, meaning the type of motion can be adjusted not only by changing the object's resistance but also by deciding the action and how the person touches the object.

Moreover, the FORCE dataset was created, containing many motion nuances from interactions with objects having 3-6 levels of resistance. A hybrid tracking system made of four Kinect RGB-D cameras and 17 Inertial Measurement Units (IMUs) was used to collect data. The dataset consists of 450 motion sequences, which add up to 192,000 frames of smooth interactions involving carrying, pushing, and pulling objects. Each frame in the dataset includes high-quality poses of both humans and objects, serving as a useful benchmark for various tasks involving human-object interactions.

#Related Work

The tasks associated with synthesizing human-object interactions have existed in computer vision for a long time. Initially, the focus was on basic human motion synthesis without much context. But in more recent works, there’s been an effort to predict static affordances within 3D scenes, mainly looking at human interactions with objects that don’t move. Many recent studies have tried to predict human movement in pre-scanned environments, training separate modules to track main movements and then generating full-body poses.

However, the quality of existing Datasets often falls short when it comes to producing realistic human motion. Research has been concentrated mainly on situations where interactions involve static objects, such as sitting or lying down on chairs. Other studies even work on simulating how a person grasps objects and moves their hands. But most of these efforts have failed to consider the important dynamic interactions between humans and moving objects.

On the other hand, there are physical simulation-based methods and kinematics-based approaches that have tried to solve this problem. For instance, some have developed frameworks that generate movements for catching and carrying techniques using egocentric perspectives. While these methods are promising, they often become too complicated, resulting in the need for various motion policies.

In contrast, kinematic approaches are generally more efficient. Among them, Neural State Machine has shown the ability to model a range of static and dynamic interactions well. Other works have focused on understanding movements in contact situations but haven't considered how the motion influences the way humans interact with objects.

Our model stands out because it pays attention to physical traits that have been neglected in previous studies, enabling the generation of distinct human-object interactions with fine details.

#FORCE Dataset

The FORCE dataset is a significant contribution to the field. It accurately captures diverse and nuanced interaction motions while considering various resistance levels. The dataset includes detailed action sequences of pushing, pulling, and carrying objects across different resistance challenges.

To collect this data, a customized tracking system was developed to overcome noise and occlusion issues. By integrating human-mounted sensors with cameras, the accuracy of the captured data improved significantly.

Each object used in the study was pre-scanned to create reference models. During data collection, the objects were strategically placed to ensure authentic movement replication under varying conditions. Each action was executed with minimal guidance to maintain natural behavior.

The dataset consists of 450 sequences covering different interaction types. Each interaction is characterized by its associated resistance, which is manipulated through the addition of weights. The design of the collection process also ensures a spread of variations, capturing different contact modes like one-handed and two-handed interactions.

#Methodology

The core idea behind the FORCE model is to synthesize diverse and nuanced human-object interactions by modeling physical traits like resistance and the applied human force. The intent is to make the model responsive to changes in the scenario. The synthesis of motion is not just reliant on object resistance but also on the type of action and the method of contact.

Our method uses two key components: a physics-aware motion network and a contact prediction network. These components work together, where the motion network generates the movements while the contact prediction network ensures the interaction's plausibility.

The physics-aware motion network learns from various information types, including human movement and object details, to synthesize future movements. The input includes the current state of the human, the object, and the physical context of the interaction. The model pays attention to the interplay between the force exerted by the human and the resistance the object presents, which helps in producing realistic movements.

The contact prediction network focuses on ensuring that the human's actions are physically plausible based on the object characteristics. For example, the way a person holds an object can shift depending on how heavy or slippery it is. This aspect is crucial for making sure the synthesized motion respects the laws of physics, leading to less collision and more realistic interactions.

#Training and Evaluation

The training process of the FORCE model involves refining the motion and contact predictions to ensure high-quality results. This is achieved through supervised learning techniques, focusing on minimizing errors related to the future human pose and interaction outcomes. The model is tested on diverse scenarios, emphasizing the need for accuracy and realism across various motion types and resistance levels.

To evaluate our model's performance, we compare it against baseline methods to assess accuracy, execution time, and diversity of generated motions. Metrics such as average per-joint error, success rate, and collision scores help to quantify how well the model performs in generating plausible interactions.

#Results and Discussions

The results demonstrate that the FORCE model surpasses previous methods in generating realistic human-object interactions. The performance in terms of accuracy and diversity is significant, indicating that our approach effectively captures the nuances of human movement in response to varying physical scenarios.

For example, when tested, the model successfully generated actions like carrying and pushing objects, adjusting the human pose based on how resistant the objects were. The ability to synthesize these motions shows the strength of the physics-aware model in practical scenarios.

Further evaluations indicate that the model achieves higher success rates in interaction tasks and minimizes collisions during motions, reinforcing its capacity for producing realistic interactions. The qualitative evaluations also reveal that the nuances in motion are preserved across different scenarios, showcasing the model's versatility.

#Conclusion

This work sets out to advance the understanding of human-object interactions by presenting a kinematic method that blends intuitive physics with human motion synthesis. The FORCE model and the accompanying dataset stand as important tools for researchers and developers in fields such as animation, virtual reality, and gaming.

By focusing on the interplay between applied force and resistance, this method successfully addresses challenges in generating diverse human movements. The dataset provides a rich resource for further exploration and development in human-object interaction modeling.

The advancements brought forth contribute to a greater range of possibilities for creating realistic human actions in various applications. Future work may expand on these findings by incorporating more dynamic scenarios and a wider variety of interactions, opening the door for richer simulations and experiences.