ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models

Authors: peiming  li
ziyi  wang
mengyuan  liu
hong  liu
chen 2024 chen
Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-28 08:58:42

Abstract: Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and diversity under scene constraints, they tend to overlook the fine-grained hand-object interactions such as contacts, resulting in inaccurate and undesired grasps. To address these challenges, we propose a controllable grasp generation task and introduce ClickDiff, a controllable conditional generation model that leverages a fine-grained Semantic Contact Map (SCM). Particularly when synthesizing interactive grasps, the method enables the precise control of grasp synthesis through either user-specified or algorithmically predicted Semantic Contact Map. Specifically, to optimally utilize contact supervision constraints and to accurately model the complex physical structure of hands, we propose a Dual Generation Framework. Within this framework, the Semantic Conditional Module generates reasonable contact maps based on fine-grained contact information, while the Contact Conditional Module utilizes contact maps alongside object point clouds to generate realistic grasps. We evaluate the evaluation criteria applicable to controllable grasp generation. Both unimanual and bimanual generation experiments on GRAB and ARCTIC datasets verify the validity of our proposed method, demonstrating the efficacy and robustness of ClickDiff, even with previously unseen objects. Our code is available at https://anonymous.4open.science/r/ClickDiff.

Main paper: 
 introduction: 
 In recent years, the modeling of hand-object interactions [1, 2, 5, 7, 11, 15, 24, 25, 30, 34, 38] has gained substantial importance due to its significant role in applications across human-computer interaction [39] , virtual reality [13, 39, 40] , robotics [3, 37, 45] , and animation [27] . Addressing the specific needs for hand-object interaction modeling emerges as a paramount concern. How do hands interact with a bowl? One can envision a variety of interaction types (e.g., "grabbing/holding") and numerous possible interaction locations (e.g., "rim/bottom"). The diversity of these interactions largely stems from the layouts of hands and objects, making the generation of accurate interactions challenging. An accurate generative model should account for factors such as which areas of the object will be touched and which parts of the hand will make contact. In contrast, a lack of thorough and precise modeling may result in unnatural and unrealistic interactions.Traditional approaches [15, 24] for hand generation have primarily focused on visibility and diversity under scene constraints. Most existing controllably generated hand-object interaction images rely on simple textual scene understandings, such as "a hand holding a cup" for input synthesis, failing to generate precise hand-object interactions due to the lack of fine-grained information. Methods Figure 1 : Previous works face issues of contact ambiguity, where an input object could lead to multiple undesired grasps, such as when a bowl is expected to be grabbed from the bottom, revealing the importance of controllable grasp generation. By manually determining contact points and specifying contact fingers, one can obtain SCM (each color represents the contact area of a different finger) by traversing the area around the clicked point. Finally, by utilizing SCM, it's possible to achieve accurate user-expected grasp.[44] utilizing contact information often depend on contact mapping applied to object point clouds to indicate contact points. However, relying solely on information learned from contact maps as constraints still leaves the issue of which areas of the hand to use for touching unaddressed. Moreover, providing contact maps for both the object and the hand simultaneously is almost an impossible task in real-world applications. Previous efforts in grasp generation have emphasized achieving visibility and diversity, often overlooking the fine-grained handobject interactions such as contacts. This oversight has led to a notable deficit in both accuracy and practicality of generated grips, especially in scenarios requiring precise and controllable synthesis. To address these challenges, we propose a controllable grasp generation task, which can realize the generation through manually specified hand and object contact point pairs. Our key insight is that the movements of hand joints are largely driven by the geometry of fingers. Explicitly modeling the contact between fingers and object sampling points during training and inference can serve as a powerful proxy guide. Employing this guidance to link the movements of hands and objects can result in more realistic and precise interactions. What's more, grip generation guided by artificially defined contact is more practical.Fig. 1 demonstrates the contact ambiguity issues present in past generation methods compared to controllable generation task with artificially designated contact points. To better model the artificially defined fine-grained contacts shown in Fig. 1 , we introduce a novel, simple and easily specified contact representation method called Semantic Contact Map. By processing point clouds of objects and hands, it obtains representations of points on the object that are touched and the fingers touching those points, specifically providing: (1) The points on the object that are touched. (2) The number of the finger touching the point. A significant advantage is that one can click to customize the Semantic Contact Map to achieve usercontrolled interactive generation. We show that utilizing Semantic Contact Map can achieve more natural and precise generation results than conventional methods that rely solely on contact map. Furthermore, to better utilize a human-specified SCM in contact supervision constraints, we propose a Dual Generation Framework based on conditional diffusion model [12, 19, 36, 43] . The denoising process of the diffusion model inherently involves constraints on the parameters of both hands, reducing unnatural and unrealistic interactions between them. Building on the aforementioned observations, this paper introduces the ClickDiff based on Semantic Contact Map to realize controllable grasp generation, which is divided into two parts: (1) We explicitly model the contact between fingers and object sampling points by specifying the locations of contact points on the object and the finger numbers contacting those points, guiding the interactions between hands and objects. Benefit from previous work utilizing contact map [6, 10, 15, 24, 33] , to embed Semantic Contact Map into the feature space for easy learning and utilization by the network, we propose the Semantic Conditional Module, which can generate plausible contact maps based on specified Semantic Contact Map and object point clouds. (2) Inspired by the grasps generation method in the RGB image domain [10] , we propose the Contact Conditional Module, which synthesizes hand grasps based on generated contact maps and object point cloud information as conditions. To better utilize Semantic Contact Map, we propose Tactile-Guided Constraint. Tactile-Guided Constraint can extract pairs in the SCM that represents touching and integrate contact information into the Contact Conditional Module by calculating the distance between the centroid of each finger's predefined set of points and the contact point on the object. Specifically, we provide a Dual Generation Framework that allows using Semantic Contact Map on a given object as a condition for generating contact maps, which then serve as a condition for generating grasps.In summary, our contributions are as follows:• We are the first to propose the controllable grasp generation task. We introduce a new contact representation method, named Semantic Contact Map. Our method enables more precise generation through user-specified or algorithmically predicted Semantic Contact Map. • We propose a Dual Generation Framework composed of Semantic Conditional Module and Contact Conditional Module. The former generates contact maps using Semantic Contact Map as conditions. The latter uitilizes Tactile-Guided Constraint, addressing the contact ambiguity issue in direct contact map generation. • We evaluate the evaluation criteria applicable to controllable grasp generation. Our approach outperforms general grasp generation baselines on the GRAB and ARCTIC datasets. Additionally, we verify its robustness for unseen and out-ofdomain objects. 
related work 2.1 controllable human-object interaction: 
 In recent years, there have been attempts to model fine-grained interactions between the human and objects. Recent methods have shifted focus towards the prediction of static human poses that are congruent with environmental constraints, particularly in scenarios involving affordances and hand-object interactions. For instance, COUCH [44] presents a comprehensive model and dataset designed to facilitate the synthesis of controllable, contact-based interactions between humans and chairs. Furthermore, there has been notable exploration into the generation of motion conditional on external stimuli, such as music [23] . TOHO [22] exemplifies this by generating realistic and continuous task-oriented human-object interaction motions through prompt-based mechanisms. Additionally, advancements in custom diffusion guidance have significantly enhanced both the controllability [14, 16, 28] and physical plausibility [41] of generated interactions. EgoEgo [21] fed head pose to a conditional diffusion model to generate the full-body pose. Nevertheless, a gap remains in concerning methods that specifically address finegrained interactions, particularly the nuanced contacts involved in hand-object interactions. Inspired by the aforementioned methods, our method enables a more precise prediction and control of grasping through user-specified or algorithmically predicted contacts.The task of generating object grasps has undergone significant evolution, greatly benefitting from the introduction of novel 3D datasets. Taheri et al. [33] notably extended this research through the development of the GRAB dataset, which not only delineates the hand's contact map but also incorporates the entire human body's interaction with objects. ARCTIC [9] introduces a dataset of two hands that dexterously manipulate objects. It contains bimanual articulation of objects such as scissors or laptops, where hand poses and object states evolve jointly in time. To ensure the generated grasps with both physical plausibility and diversity, the majority of existing models employ a Conditional Variational Autoencoder (CVAE) framework to sample hand MANO parameters [15, 29, 32, 33, 35] or hand joints [17] , thus modeling the grasp variability primarily within the hand's parameter space. Given an input object, liu et al. propose a novel approach utilizing a conditional generative model based on CVAE, named ContactGen [24] , which, coupled with model-based optimization, predicts diverse and geometrically feasible grasps. Meanwhile, GraspTTA [15] introduces an innovative self-supervised task leveraging consistency constraints, allowing for the dynamic adjustment of the generation model even during testing time. Despite these advances, the CVAE models often overfit to prevalent grasp patterns due to the human hand's high degree of freedom, leading to the production of unrealistic contacts and shapes. To address these challenges, our work employs a conditional diffusion model framework, which uniquely focuses on improving the fidelity of generation through user-specified contacts. 
overview: 
 In this work, we primarily aim to answer two questions: (1) How can we characterize fine-grained contact information simply and efficiently to achieve controllable grasp generation? (2) How can we better utilize the related contact information to guide grasp generation? To address question (1), in Sec. 4.1, we propose a controllable contact representation Semantic Contact Map (SCM), which simultaneously represents the fine-grained contacts between fingers and  
method 4.1 semantic contact map (scm): 
 Following [9] , the contact map 𝐶 ∈ R 𝑁 ×1 represented by heatmap, each 𝑐 𝑖 ∈ 𝐶 ranges between [0, 1], represents the distance to the nearest finger point at each object point. Intuitively, a contact map shows which part of an object might be touched by a hand. However, relying solely on contact maps is insufficient for modeling complex grasps due to ambiguity about how and where the hands touch the object. To overcome this issue caused by the lack of finegrained contact representations, inspired by [18, 24] , we propose the following Semantic Contact Map:EQUATIONwhere 𝑜 represents object sampling points ∈ 𝑅 2048 , 𝑓 represents finger indices ∈ 𝑅 5 , 𝑂 represents the object point clouds ∈ 𝑅 2048×3 , 𝐻 represents the hand point clouds ∈ 𝑅 778×3 . Different from the part map (one-hot vector whose value is the nearest hand labels) proposed by ContactGen [24] for the entire hand reliant only on the SDF, in our understanding, the contact relationship between fingers and object points can be many-tomany. Moreover, utilizing finger control and emphasizing the finger number rather than the hand part is more convenient and flexible for user-defined controllable generation. Therefore, we heuristically represent finger-object point pairs with a distance below the threshold in SCM as 0. At the same time, Tactile-Guided Constraint designed for SCM is used to better learn embedding conditions. Fig. 3 shows an example of SCM. By processing point clouds of objects and hands, SCM contains information on whether points on the object are touched and the numbering of the fingers touching those points, which is more important for interactive grasp control. Apart from being generated through Fingertouch-Analysis, the most important aspect of SCM is that it can be customized by the user by clicking and traversing the precalculated weights of the area around the clicked points, hence we can achieve controllable grasp generation.Inspired by controllable contact-based method [10, 44] , we use conventional contact maps during generation to ensure that the model learns the correct distribution. However, it's challenging to provide realistic contact maps in practical applications and relying solely contact maps introduces ambiguity regarding which part of the hand touches and how it makes contact. Furthermore, past research [15, 18, 24] has shown that single-stage models struggle to generate high-quality grasps. Therefore, we propose a Dual Generation Framework, comprising a Semantic Conditional Module and a Contact Conditional Module. We first use Semantic Contact Map to infer the most probable contact maps based on SCM, then generate controllable grasps under the guidance of Tactile-Guided Constraint. As a result, we achieve controllable generation by starting from a user-defined SCM. Fig. 2 summarizes our process for training and testing.In Semantic Conditional Module, the parameters 𝑪 ∈ R 2048 are composed of the object's contact map parameters. We use a conditional generation model to infer probable contact maps 𝐶 based on user-specified or algorithmically predicted Semantic Contact Maps. The process is as follows:EQUATIONwhere C𝑛 denotes the contact map at noise level n, 𝑂 denotes the object point clouds and CGM denotes conditional generation model. In Contact Conditional Module, MANO parameters [29] 𝑴 ∈ R 61 are composed of the hand's MANO parameters. We also use a conditional generation model based on contact maps predicted by the Semantic Conditional Module and constrained by Semantic Contact Maps to infer MANO parameters. The training of two modules can be carried out independently. To enable the model to learn additional finger information from simple contact maps, we design Tactile-Guided Constraint, which will be detailed in the next section. The entire process can be represented as:EQUATIONwhere M𝑛 represents MANO parameters at nosie level n, 𝑂 represents object point clouds. In the Contact Conditional Module, simply utilizing contact map as a condition introduces ambiguity regarding which part of the hand is in contact with the object. Based on the Semantic Contact Map, we tailored Tactile-Guided Constraint as follows:EQUATIONwhere (𝑖 𝑘 , 𝑗 𝑘 ) represents 𝑘 𝑡ℎ pair of indices in the SCM that represents touching. 𝑖 𝑘 denotes the index of points. 𝑗 𝑘 denotes the index of fingers. 𝑂 (𝑖 𝑘 ) denotes coordinates of the object point. 𝐻 ( 𝑗 𝑘 ) denotes coordinates of the centroid for each touched finger. For each finger, we predefine a set of contact points that are weighted differently depending on the distance to the inner surface of the finger. Then a centroid selection algorithm computes the weighted average value from the set to determine the touch centroid for that finger. 𝑁 denotes the number of contact pairs designated within the SCM. The Tactile-Guided Constraint loss (L 𝑇𝐺𝐶 ) specifically targets the vertices within the finger sets proximal to the object's surface, ensuring that fingers accurately align with the designated groundtruth contact areas by accurately indexing the point pairs in the SCM and calculating the distance between the centroid of each finger's predefined set of points and the contact point on the object. We adopt the 𝐿 2 distance to compute the distance error between object and hand point clouds.We denote the reconstruction loss in the training of Semantic Conditional Module between the predicted contact map and the ground-truth as:EQUATIONThe reconstruction loss on MANO parameters in the training of Contact Conditional Module is defined in a similar way as:EQUATIONAt the same time, we design a contact-map-based loss for Semantic Conditional Module training, followed as:EQUATION)EQUATIONwhere 𝐵 𝑜 represents the binary map's value when the distance 𝐶 𝑜 between a sampled point on the hand and a corresponding object point 𝑜 is less than the predefined threshold 𝜏 threshold and ⊙ denotes matrix dot product. The efficacy of the L 𝐶 loss function is attributed to its focus on hand-object point pairs, which are systematically selected based on the contact map during the training phase. Furthermore, to enhance the accuracy of hand posture modeling within Contact Conditional Module, we employ a loss function that measures the discrepancy between predicted vertices Ṽ and the ground-truth vertices 𝑉 , which is mathematically represented as:EQUATION)In a short summary, the whole loss for training the Semantic Conditional Module is:EQUATION)And the training loss of Contact Conditional Module is defined as:EQUATION)5 EXPERIMENT 5.1 Experimental Details 5.1.1 Datasets: We employ the GRAB dataset [33] to train the ClickDiff and assess the efficacy of our proposed approach in a unimanual grip scenario. The GRAB [33] , which comprises real human grasp data for 51 objects across 10 different subjects, is instrumental in our analysis. Notably, the dataset's test set consists of six objects that are not encountered during training, presenting an opportunity to assess the model's generalization capabilities. Furthermore, ARCTIC [9] is a dataset of dexterously bimanual manipulating articulated objects. It facilitates our exploration into the realm of dexterous bimanual manipulation. 
implementation details:: 
 The ClickDiff is trained on the GRAB [33] and ARCTIC [9] datasets, which takes N = 2048 points sampled from GRAB object surface and N = 600 points sampled from ARCTIC object surface as input. We extract object features using PointNet [4] and adopt the standard Adam optimizer [20] with a learning rate of 1 × 10 -5 and betas of 0.9 and 0.999. We train our model with batch size of 256 for 600k steps. The loss weights are 𝜆 𝛼 = 2, 𝜆 𝛽 = 0.5 and 𝜆 𝜃 = 1. The network is implemented in PyTorch and trained on a single NVIDIA RTX 3090 GPU.Our objective is to synthesize highly accurate, contact-based, fine-grained hand grips on objects to achieve controllable grasp generation. Thus, we evaluate the evaluation criteria applicable to controllable grasp generation. To evaluate the performance of our model, following [8, 9, 26, 31, 42] , we use three metrics previously utilized (MPJPE, MRRPE, CDev) along with a custom metric (Success Rate). To ensure that contacts made by hands touching or holding objects are controllable and accurate, we measure precision using metrics known as Contact Deviation (CDev) and Success Rate. Moreover, capturing the correct posture of the hand and the position of its bones accurately when the hand moves or interacts with an object is crucial. We check these details utilizing Mean Per-Joint Position Error (MPJPE) and Mean Relative-Root Position Error (MRRPE). The definitions are as follows:• Mean Per-Joint Position Error (MPJPE): The 𝐿 2 distance between the 21 predicted and ground-truth joints for each hand after subtracting its root. • Mean Relative-Root Position Error (MRRPE): Measures the root translation between the hand and object:EQUATIONwhere 𝑎, 𝑏 ∈ {𝑙, 𝑟, 𝑜 }, with 𝑙, 𝑟, 𝑜 representing the left hand, right hand, and the object, respectively, J ∈ IR 3 is the groundtruth root joint position and Ĵ is the predicted one. We only adopt this metric on the ARCTIC dataset to measure the bimanual grasps. where {(h 𝑖 , o 𝑖 )} 𝐶 𝑖=1 are 𝐶 pairs of in-contact hand-object vertices with distance less than 3mm, and {( ĥ𝑖 , ô𝑖 )} 𝐶 𝑖=1 are the corresponding predictions.• Success Rate: Success rate is defined as the ratio of the size of the union of the point set 𝐴 in the generated hand point Table 1 : Quantitative comparison results of state-of-the-art methods and ours on the GRAB dataset [33] on different out-ofdomain objects. GN denotes GrabNet [33] , GL denotes GOAL [32] , GA denotes GraspTTA [15] and CG denotes ContactGen [24] .CDev (mm) ↓ Success Rate (%) ↑ GN [33] GA [15] GL [32] CG [24] GN [33] GA [15] GL [32] CG [24] GN [33] GA [15] GL [32] clouds that are in contact with the target object to the size of the point set 𝐵 in the real hand point cloud that are in contact with the object, relative to the size of set 𝐴. This metric aims to quantify the contact quality of the generated hand grasps.Results on GRAB dataset. We evaluate the generalization capability of our models on the GRAB dataset [33] . The dataset's testset, consisting of six objects not previously encountered during training, serves as a benchmark to evaluate our model's adaptability to new scenarios. Tab. 1 shows comparison of our method with GrabNet [33] , GOAL [32] , ContactGen [24] and GraspTTA [15] on the GRAB dataset [33] , our method achieves performance in each metric on almost all objects. Fig. 4 illustrates a significant reduction in contact deviation when implementing the Semantic Contact Map, revealing the importance of controllable grasp generation. As GraspTTA struggles to produce valid grasps for unseen objects, the contact deviation is substantial while ContactGen often produces unnatural distortions. The results reveal that the hands produced by both GraspTTA and ContactGen lack coordination and deviate significantly from the expected contact areas. In comparison to them, our method achieves notably lower penetration and better stability, which is the closest to ground-truth.Results on ARCTIC dataset. Tab. 2 shows comparison of our method with GraspTTA [15] on the ARCTIC dataset, examining performance on both in-domain and out-of-domain objects. When the unimanual generation method is applied to the bimanual generation, the metric between two hands has a huge gap and incongruous effect, and tends to fail. On the contrary, our approach We first perform ablation studies on GRAB dataset [33] for evaluating the proposed Semantic Contact Map in sec 5. [15, 24] has shown that single-stage generation exhibits limitations in accurately generating both hand posture and contact position. This analysis confirms the effectiveness of our Dual Generation Framework, particularly when augmented by SCM.Impact of loss selection. We conduct another ablation study to assess the contribution of different losses. The results are shown in Tab. 5. Applying the Tactile-Guided Constraint L 𝑇𝐺𝐶 effectively ensures that the fingers align with the designated groundtruth contact regions. Notably, the introduction of L 𝑇𝐺𝐶 results in a significant reduction in joint displacement and improvements in contact metrics, exemplified by a 6.11 mm decrease in Contact Deviation (CDev). Experiments demonstrate that our L 𝑇𝐺𝐶 constrains the contact position of fingers in the Contact Conditional Module, which solves the contact ambiguity problem well. After adopting the vertice loss L 𝑉 , joint and finger related posture metrics, e.g., MPJPE stability decrease. Meanwhile, adding the contact map loss L 𝐶 for Semantic Conditional Module improves contact and displacement related metrics, e.g., CDev and Success Rate, attributable to its focus on hand-object point pairings during training. These results imply that L 𝑇𝐺𝐶 , L 𝑉 and L 𝐶 , in concert, improve the grasp generation, in alignment with the intended design of these loss functions.We analyze the influence of the addition of controllable contact conditions on time. In terms of total time, our controllable grasp generation time has increased by only 30% (from 13.5 hours to 17.5 hours) compared to the model devoid of any contact conditions with batch size of 128 for 600k steps on a single NVIDIA RTX 3090 GPU. 
conclusion: 
 In this work, we introduce ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models.To solve the contact ambiguity problem and achieve controllable grasp generation, we propose a simple and efficient Semantic Contact Map that can be defined by users by clicking. At the same time, Dual Generation Framework and Tactile-Guided Constraint are proposed to utilize SCM. Our method demonstrates superior performance to existing grasp generation methods, both qualitatively and quantitatively. 
 
 Appendix:  


Decision: This paper introduces a novel method for generating controllable hand grasps using diffusion models and a semantic contact map (SCM). The main innovation lies in the separation of the five fingers in the contact map, which allows for more detailed and semantic-rich control over the grasp generation process. By conditioning the diffusion model on this SCM, the method can generate realistic and physically plausible hand-object interactions. The proposed approach shows significant improvements over state-of-the-art methods in terms of the realism and applicability of the generated grasps, as demonstrated through experiments on the GRAB and ARCTIC datasets.

The paper is well-received for its clarity, thorough experimentation, and the introduction of a dual learning framework that eases the learning process. Notably, it enables user-controlled grasp generation through simple interactions with the contact points on the mesh, catering to specific user expectations and enhancing the grasp's relevance for downstream tasks. This method may contribute to multimedia and multimodal processing, particularly in enhancing interactive experiences in VR, AR, and robotics by facilitating more nuanced human-computer and human-robot interactions. All reviewers have agreed to accept the paper.

Reviews: review: paper_summary: paper_summary: This paper proposes a semantic contact map for controllable grasp generation with a diffusion model. Basically, the main idea is to separate the five fingers in the contact map and employ a diffusion model to generate the grasp pose with the condition on the contact map. The writing is easy to follow and the experimental results show some good results.
strength_weakness: strength_weakness: - well-written paper and easy to follow. The main idea is clearly presented.
- The motivation makes sense to me. Using five individual contact maps introduces more semantic information to the generation process.
limitations: limitations: - The experimental results are not sufficient. HOI4D is missing from the evaluation and it is quite well known dataset. It is recommended to involve this dataset. In addition, the training/testing splitting is not clear for GRAB and ARCTIC datasets. This makes the results unconvincing. 
- only [16] and [25] are involved for comparison. There are many related works that the authors have shown inspiration in their paper. These works should be involved, e.g. [11,44,19,1,9,10,27,32]. Otherwise, the evaluation result is quite selective.
paper_summary: paper_summary: This paper proposes a diffusion model to generate realistic hand object interactions. It introduces a nvoel representation SCM to reflect the contact between the hand and the object. Based on SCM, it can infer the contact map and finally generate realistic grasps. In the experimental part, the propsed method demonstrates better performance against state-of-the-art methods.
strength_weakness: strength_weakness: - It introduces a novel SCM representation to analyze the contact patterns between the hand and the object.
- The proposed dual learning framework together with tactile-guided constraint allieviate the difficulty of the learning process.
- The generation process is easy to control and shows impressive qualitative results.
limitations: limitations: - Missing failure cases of the proposed method. Though the method shows good performance on unseen object at test time, it would be good to also put some failure cases to help readers know what is the limitation and bottleneck for the proposed method.
- In Table 1, the proposed method does not show consistent advantages over previous methods. Especially for flying pan, the results seem much worse than [16] on Cdev and success rate. What is the reason behind this phenomenon?
paper_summary: paper_summary: This paper proposes a novel method that generates the hand grasp given the contact points on the mesh by clicking. Previous methods, such as ContactGen and GraspTTA, can generate the physical-plausible "touch", but cannot generation is very random, and cannot make sure the generated grasp is meaningful for the downstream tasks. This method can control the generation with user expectations. Experiments show that the results on GRAB and ARCTIC datasets, further demonstrate the efficacy and robustness of ClickDiff, even with previously unseen objects.
strength_weakness: strength_weakness: 1. This is a solid work that tackles a real-world problem.
2. The motivation and paper writing is good.
3. The paper also conducts a thorough comparison with various methods using multiple test datasets and unseen objects.
limitations: limitations: Table 1's comparison is insufficient and somewhat unfair because GraspTTA and ContactGen do not provide contact click points, while our method does. This means our method benefits from having ground truth information in a different format. As a result, this experiment only demonstrates our motivation but does not conclusively show that our method is superior. I suggest comparing the results using the framework from ContactGen or ContactTTA given a click. This comparison will effectively demonstrate that our framework outperforms VAE/GAN-based methods.
paper_summary: paper_summary: This work significantly contributes to multimedia/multimodal processing by advancing the state-of-the-art in hand grasping generation through the integration of diffusion models. By generating more natural and accurate hand grasps, this research enhances the interactivity and realism in multimedia applications such as virtual reality (VR), augmented reality (AR), and robotics. Specifically, the controllable grasp generation facilitates more nuanced and realistic human-computer and human-robot interactions, allowing for a more immersive user experience in VR and AR scenarios, and more effective grasp execution in robotics.  Moreover, the use of diffusion models for hand grasp generation opens up new possibilities for synthesizing multimodal data. Thus, this work not only pushes the boundaries of what's possible in hand grasp synthesis but also contributes to the broader field of multimedia/multimodal processing by enhancing the quality of interactive experiences.
strength_weakness: strength_weakness: novelty
limitations: limitations: insufficient evaluation