A high-order graph convolutional network for homomorphic and heterogeneous skeletal motion retargeting
Objective Skeletal motion retargeting is a key technology that involves adapting skeletal motion data from a source character,after suitable modification,to a target character with a different skeleton structure,thereby ensuring that the target character performs actions identical to the source.This process,which is particularly crucial in animation pro-duction and game development,can greatly promote the reuse of existing motion data and significantly reduce the need to create new motion data from scratch.Skeletal motion data have an inherently strong relationship with a character's skeleton structure,and the core challenge in retargeting lies in extracting motion data features that are independent of the source skeleton and solely embody the essence and pattern of the action.The complexity in this process increases markedly during practical applications,especially when the source and target characters stem from distinct datasets(e.g.,translating motion capture data from real human subjects onto virtual animated characters with heterogeneous skeletal structures).The differences between such datasets extend beyond mere skeletal disparities and may encompass inconsistencies in capturing equipment,physiological variations among individuals,and diverse action execution environments.Collectively,these fac-tors produce significant discrepancies between the source and target characters in terms of global movement ranges,joint angle variation range,and other motion attributes,thus posing formidable challenges for retargeting algorithms.This paper addresses the problem of overcoming data heterogeneity to enable a precise motion retargeting from real human motion data to heterogeneous yet topologically equivalent virtual animated characters.To this end,this paper proposes several strate-gies for feature separation and high-order skeletal convolution operators.Method During the data preprocessing stage,fea-ture separation is applied on the motion data to isolate those components that are independent of the skeletal structure.This approach significantly reduces the complexity of the data and consequently reduce the difficulty of the heterogeneous retar-geting task and facilitate the attainment of superior retargeting outcomes.Moreover,given the high sensitivity of motion retargeting tasks to local features,this paper delves into the distance information between joints and,in conjunction with higher-order graph convolution theory,introduces innovative improvements to conventional skeletal convolution methods,ultimately proposing a novel high-order skeletal convolution operator.In high-order graph convolutional operations,the employed adjacency matrices of higher powers encapsulate a more abundant and tangible information profile.These matri-ces not only encompass fundamental structural information,i.e.,direct adjacencies between nodes,but may also be extended to embody the multi-level distance characteristics among nodes.This new operator harnesses the rich adjacency relationships and distance information encapsulated within higher-order adjacency matrices,thereby enabling convolution operations to thoroughly and comprehensively extract the intrinsic structural features of the skeleton and enhancing the accuracy and visual effect of the retargeting results.Result In the heterogeneous motion retargeting task,the proposed algo-rithm demonstrates a significant improvement(38.6％)in retargeting accuracy compared with the current state-of-the-art methods when evaluated using the synthetic animation dataset Mixamo.To further understand the model's characteristics,the root joint errors are examined to examine its precision in handling root joint position.Results show that relative to extant methods,the proposed algorithm reduces the root joint position errors by 35.5％,hence substantiating its exceptional capa-bility in addressing retargeting tasks with large ranges of root joint position variations.This algorithm also demonstrates its applicability and superiority in homogeneous motion retargeting tasks,achieving a 74.8％higher accuracy compared with extant methods.In practical applications,when applying real-world motion data captured from humans to the retargeting of virtual animated characters in aheterogeneous context,our algorithm excels at delivering high levels of authenticity in repro-ducing specific actions and significantly reducing retargeting errors.Conclusion This paper presents a framework that is capable of handling challenging motion retargeting tasks between heterogeneous yet topologically equivalent skeletons.When the training data originate from two significantly diverse datasets,the proposed data preprocessing methods and high-order skeletal convolutional operators enable the neural network models to effectively extract motion features from the source data and integrate them into the target skeleton,thereby generating skeletal motion data for the target character.By separating features of the motion data that are independent of the skeleton structure,the proposed model can focus on structure-relevant information,thereby effectively decoupling motion information from structural details and achieving motion retargeting.Additionally,by assigning different weights to joints at varying distances,the high-order skeletal convo-lutional operators gather enhanced skeletal structural information to improve network performance.
deep learningmotion retargetinggraph convolutional networkautoencoderHuman3.6M motion data
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