The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine depiction and sufficient intensity, leading to the synthesized motions that either (a) semantically compliant but uncontrollable over specific pose details, or (b) even deviates from the provided descriptions, bringing animators with undesired cases. In this paper, we propose DiffKFC, a conditional diffusion model for text-driven motion synthesis with KeyFrames Collaborated, enabling realistic generation with collaborative and efficient dual-level control: coarse guidance at semantic level, with only few keyframes for direct and fine-grained depiction down to body posture level. Unlike existing inference-editing diffusion models that incorporate conditions without training, our conditional diffusion model is explicitly trained and can fully exploit correlations among texts, keyframes and the diffused target frames. To preserve the control capability of discrete and sparse keyframes, we customize dilated mask attention modules where only partial valid tokens participate in local-to-global attention, indicated by the dilated keyframe mask. Additionally, we develop a simple yet effective smoothness prior, which steers the generated frames towards seamless keyframe transitions at inference. Extensive experiments show that our model not only achieves state-of-the-art performance in terms of semantic fidelity, but more importantly, is able to satisfy animator requirements through fine-grained guidance without tedious labor.

Domain shift is a challenge for supervised human pose estimation, where the source data and target data come from different distributions. This is why pose estimation methods generally perform worse on the test set than on the training set. Recently, test-time adaptation has proven to be an effective way to deal with domain shift in human pose estimation. Although the performance on the target domain has been improved, existing methods require a large number of weight updates for convergence, which is time-consuming and brings catastrophic forgetting. To solve these issues, we propose a meta-auxiliary learning method to achieve fast adaptation for domain shift during inference. Specifically, we take human pose estimation as the supervised primary task, and propose body-specific image inpainting as a self-supervised auxiliary task. First, we Jointly train the primary and auxiliary tasks to get a pre-trained model on the source domain. Then, meta-training correlates the performance of the two tasks to learn a good weight initialization. Finally, meta-testing adapts the meta-learned model to the target data through self-supervised learning. Benefiting from the meta-learning paradigm, the proposed method enables fast adaptation to the target domain while preserving the source domain knowledge. The carefully designed auxiliary task better pays attention to human-related semantics in a single image. Extensive experiments demonstrate the effectiveness of our test-time fast adaptation.

In the academic field, the research on human motion pre-diction tasks mainly focuses on exploiting the observed in-formation to forecast human movements accurately in the near future horizon. However, a significant gap appears when it comes to the application field, as current models are all trained offline, with fixed parameters that are inher-ently suboptimal to handle the complex yet ever-changing nature of human behaviors. To bridge this gap, in this pa-per, we introduce the task of online meta adaptation for hu-man motion prediction, based on the insight that finding “smart weights” capable of swift adjustments to suit dif-ferent motion contexts along the time is a key to improving predictive accuracy. We propose MoML, which ingeniously borrows the bilevel optimization spirit of model-agnostic meta-learning, to transform previous predictive mistakes into strong inductive biases to guide online adaptation. This is achieved by our MoAdapter blocks that can learn er-ror information by facilitating efficient adaptation via a few gradient steps, which fine-tunes our meta-learned “smart” initialization produced by the generic predictor. Considering real-time requirements in practice, we further propose Fast-MoML, a more efficient variant of MoML that features a closed-form solution instead of conventional gradient up-date. Experimental results show that our approach can ef-fectively bring many existing offline motion prediction mod-els online, and improves their predictive accuracy.

Generating realistic human motions with high framerate is an underexplored task, due to the varied framerates of training data, huge memory burden brought by high framerates and slow sampling speed of generative models. Recent advances make a compromise for training by downsampling high-framerate details away and discarding low-framerate samples, which suffer from severe information loss and restricted-framerate generation. In this paper, we found that the recent emerging paradigm of Implicit Neural Representations (INRs) that encode a signal into a continuous function can effectively tackle this challenging problem. To this end, we introduce NeRM, a generative model capable of taking advantage of varied-size data and capturing variational distribution of motions for high-framerate motion synthesis. By optimizing latent representation and a auto-decoder conditioned on temporal coordinates, NeRM learns continuous motion fields of sampled motion clips that ingeniously avoid explicit modeling of raw varied-size motions. This expressive latent representation is then used to learn a diffusion model that enables both unconditional and conditional generation of human motions. We demonstrate that our approach achieves competitive results with state-of-the-art methods, and can generate arbitrary framerate motions. Additionally, we show that NeRM is not only memory-friendly, but also highly efficient even when generating high-framerate motions.

Previous works on human motion prediction follow the pattern of building an extrapolation mapping between the sequence observed and the one to be predicted. However, the inherent difficulty of time-series extrapolation and complexity of human motion data still result in many failure cases. In this paper, we explore a longer horizon of sequence with more poses following behind, which breaks the limit in extrapolation problems that data/information on the other side of the predictive target is completely unknown. As these poses are unavailable for testing, we regard them as a privileged sequence, and propose a Two-stage Privileged Knowledge Distillation framework that incorporates privileged information in the forecasting process while avoiding direct use of it. Specifically, in the first stage, both the observed and privileged sequence are encoded for interpolation, with Privileged-sequence-Encoder (Priv-Encoder) learning privileged knowledge (PK) simultaneously. Then, in the second stage where privileged sequence is not observable, a novel PK-Simulator distills PK by approximating the behavior of Priv-Encoder, but only taking as input the observed sequence, to enable a PK-aware prediction pattern. Moreover, we present a One-stage version of this framework, using Shared Encoder that integrates the observation encoding in both interpolation and prediction branches to realize parallel training, which helps produce the most conducive PK to prediction pipeline. Experimental results show that our frameworks are model-agnostic, and can be applied to existing motion prediction models with encoder-decoder architecture to achieve improved performance.

Predicting high-fidelity future human poses, from a historically observed sequence, is crucial for intelligent robots to interact with humans. Deep end-to-end learning approaches, which typically train a generic pre-trained model on external datasets and then directly apply it to all test samples, emerge as the dominant solution to solve this issue. Despite encouraging progress, they remain non-optimal, as the unique properties (e.g., motion style, rhythm) of a specific sequence cannot be adapted. More generally, once encountering out-of-distributions, the predicted poses tend to be unreliable. Motivated by this observation, we propose a novel test-time adaptation framework that leverages two self-supervised auxiliary tasks to help the primary forecasting network adapt to the test sequence. In the testing phase, our model can adjust the model parameters by several gradient updates to improve the generation quality. However, due to catastrophic forgetting, both auxiliary tasks typically have a low ability to automatically present the desired positive incentives for the final prediction performance. For this reason, we also propose a meta-auxiliary learning scheme for better adaptation. Extensive experiments show that the proposed approach achieves higher accuracy and more realistic visualization.

Stochastic human motion prediction aims to forecast multiple plausible future motions given a single pose sequence from the past. Most previous works focus on designing elaborate losses to improve the accuracy, while the diversity is typically characterized by randomly sampling a set of latent variables from the latent prior, which is then decoded into possible motions. This joint training of sampling and decoding, however, suffers from posterior collapse as the learned latent variables tend to be ignored by a strong decoder, leading to limited diversity. Alternatively, inspired by the diffusion process in nonequilibrium thermodynamics, we propose MotionDiff, a diffusion probabilistic model to treat the kinematics of human joints as heated particles, which will diffuse from original states to a noise distribution. This process not only offers a natural way to obtain the "whitened'' latents without any trainable parameters, but also introduces a new noise in each diffusion step, both of which facilitate more diverse motions. Human motion prediction is then regarded as the reverse diffusion process that converts the noise distribution into realistic future motions conditioned on the observed sequence. Specifically, MotionDiff consists of two parts: a spatial-temporal transformer-based diffusion network to generate diverse yet plausible motions, and a flexible refinement network to further enable geometric losses and align with the ground truth. Experimental results on two datasets demonstrate that our model yields the competitive performance in terms of both diversity and accuracy.

Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration. Current works simplify the task of predicting human motions into a one-off process of forecasting a short future sequence (usually no longer than 1 second) based on a historical observed one. However, such simplification may fail to meet practical needs due to the neglect of the fact that motion prediction in real applications is not an isolated “observe then predict” unit, but a consecutive process composed of many rounds of such unit, semi-overlapped along the entire sequence. As time goes on, the predicted part of previous round has its corresponding ground truth observable in the new round, but their deviation in-between is neither exploited nor able to be captured by existing isolated learning fashion. In this paper, we propose DeFeeNet, a simple yet effective network that can be added on existing one-off prediction models to realize deviation perception and feedback when applied to consecutive motion prediction task. At each prediction round, the deviation generated by previous unit is first encoded by our DeFeeNet, and then incorporated into the existing predictor to enable a deviation-aware prediction manner, which, for the first time, allows for information transmit across adjacent prediction units. We design two versions of DeFeeNet as MLP-based and GRU-based, respectively. On Human3.6M and more complicated BABEL, experimental results indicate that our proposed network improves consecutive human motion prediction performance regardless of the basic model.

Current motion forecasting approaches typically train a deep end-to-end model from the source domain data, and then apply it directly to target subjects. Despite promising results, they remain non-optimal, due to privacy considerations, the test person and his/her natural properties (e.g., behavioral trait) are typically unseen in training. In this case, the source pre-trained model has a low ability to adapt to these out-of-source characteristics, resulting in an unreliable prediction. To tackle this issue, we propose a novel helper-predictor test-time personalization approach (H/P-TTP), which allows for a generalizable representation of out-of-source subjects to gain more realistic predictions. Concretely, the helper is preceded by explicit and implicit augmenters, where the former yields noisy sequences to improve robustness, while the latter is to generate novel-domain data with an adversarial learning paradigm. Then, the domain-generalizable learning is achieved where the helper can extract cross-subject invariant-knowledge to update the predictor. At test time, given a new person, the predictor is able to be further optimized to empower personalized capabilities to the specific properties. Extensive experiments show that with H/P-TTP, the existing models are significantly improved for various unseen subjects. The project page is available at https://sites.google.com/view/hp-ttp.

ABSTRACT Content‐based human motion retrieval is important for animators with the development of motion editing and synthesis, which need to search similar motions in large databases. Obtaining text‐based representation from quantization of mocap data turned out to be efficient. It becomes a fundamental step of many researches in human motion analysis. Geometric features are one of these techniques, which involve much prior knowledge and reduce data redundancy of numerical data. We describe geometric features as basic unit to define human motions (also called mo‐words) and view a human motion as a generative process. Therefore, we obtain topic motions, which possess more semantic information using latent Dirichlet allocation by learning from massive training examples in order to understand motions better. We combine probabilistic model with human motion retrieval and come up with a new representation of human motions and a new retrieval framework. Our experiments demonstrate its advantages, both for understanding motions and retrieval. Copyright © 2011 John Wiley & Sons, Ltd.