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1. Provably efficient Third-person imitation from Offline Observation

   Zweig, Aaron; Bruna, Joan (July 2020, Uncertainty in artificial intelligence)

   Domain adaptation in imitation learning represents an essential step towards improving gen- eralizability. However, even in the restricted setting of third-person imitation where transfer is between isomorphic Markov Decision Processes, there are no strong guarantees on the perfor- mance of transferred policies. We present problem-dependent, statistical learning guarantees for third-person imitation from observation in an offline setting, and a lower bound on performance in the online setting. 

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2. Language Use and Development in Third-Person Singular Contexts: Assessment Implications

   https://doi.org/https://doi.org/10.1044/2020_LSHSS-19-00109  

   Newkirk-Turner, Brandi L.; Green, Lisa (January 2021, Language speech hearing services in schools)

   Storkel, Holly L; Mills, Monique M (Ed.)

   Purpose: The purpose of this assessment-focused clinical focus article is to increase familiarity with African American English (AAE)–speaking children’s pattern of language use in third- person singular contexts and to discuss implications for speech- language assessments of developing AAE-speaking children. Method: The clinical focus draws on descriptive case study data from four typically developing child speakers of AAE who are between the ages of 3 and 5 years. The children’s data from three different sources—sentence imitation, story retell, and play-based language samples—were subjected to linguistic analyses. Results: The three sources of linguistic data offered different insights into the children’s production of –s and other linguistic patterns in third-person singular contexts. Conclusions: This study underscores the importance of exploring developing child AAE from a descriptive approach to reveal different types of information about patterns of morphological marking in different linguistic contexts, which is crucial in assessing developing AAE. Implications for language assessment are discussed. 

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3. Embodied Third-Person Virtual Locomotion using a Single Depth Camera

   https://doi.org/10.20380/GI2021.24  

   Aniruddha Prithul, Isayas Berhe (May 2021, Proceedings of Graphics Interface 2021)

   null (Ed.)

   Third-person is a popular perspective for video games, but virtual reality (VR) seems to be primarily experienced from a first-person point of view (POV). While a first-person POV generally offers the highest presence; a third-person POV allows users to see their avatar; which allows for a better bond, and the higher vantage point generally increases spatial awareness and navigation. Third-person locomotion is generally implemented using a controller or keyboard, with users often sitting down; an approach that is considered to offer a low presence and embodiment. We present a novel thirdperson locomotion method that enables a high avatar embodiment by integrating skeletal tracking with head-tilt based input to enable omnidirectional navigation beyond the confines of available tracking space. By interpreting movement relative to an avatar, the user will always keep facing the camera which optimizes skeletal tracking and keeps required instrumentation minimal (1 depth camera). A user study compares the performance, usability, VR sickness incidence and avatar embodiment of our method to using a controller for a navigation task that involves interacting with objects. Though a controller offers a higher performance and usability, our locomotion method offered a significantly higher avatar embodiment. 

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4. Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning

   Luo, M; Xue, Z; Dimakis, A; Grauman, K (June 2025, CVPR 2025)

   Egocentric and exocentric perspectives of human action differ significantly, yet overcoming this extreme viewpoint gap is critical in augmented reality and robotics. We propose VIEWPOINTROSETTA, an approach that unlocks large-scale unpaired ego and exo video data to learn clip-level viewpoint-invariant video representations. Our framework introduces (1) a diffusion-based Rosetta Stone Translator (RST), which, leveraging a moderate amount of synchronized multi-view videos, serves as a translator in feature space to decipher the alignment between unpaired ego and exo data, and (2) a dual encoder that aligns unpaired data representations through contrastive learning with RST-based synthetic feature augmentation and soft alignment. To evaluate the learned features in a standardized setting, we construct a new cross-view benchmark using Ego-Exo4D, covering cross-view retrieval, action recognition, and skill assessment tasks. Our framework demonstrates superior cross-view understanding compared to previous view-invariant learning and ego video representation learning approaches, and opens the door to bringing vast amounts of traditional third-person video to bear on the more nascent first-person setting. 

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5. Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization

   https://doi.org/10.1145/3637840  

   Balcan, Maria-Florina; Dick, Travis; Sandholm, Tuomas; Vitercik, Ellen (April 2024, Journal of the ACM)

   Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial and non-convex problems. For example, they are the foremost method for solving (mixed) integer programs and constraint satisfaction problems. Tree search algorithms come with a variety of tunable parameters that are notoriously challenging to tune by hand. A growing body of research has demonstrated the power of using a data-driven approach to automatically optimize the parameters of tree search algorithms. These techniques use atraining setof integer programs sampled from an application-specific instance distribution to find a parameter setting that has strong average performance over the training set. However, with too few samples, a parameter setting may have strong average performance on the training set but poor expected performance on future integer programs from the same application. Our main contribution is to provide the firstsample complexity guaranteesfor tree search parameter tuning. These guarantees bound the number of samples sufficient to ensure that the average performance of tree search over the samples nearly matches its future expected performance on the unknown instance distribution. In particular, the parameters we analyze weightscoring rulesused for variable selection. Proving these guarantees is challenging because tree size is a volatile function of these parameters: we prove that, for any discretization (uniform or not) of the parameter space, there exists a distribution over integer programs such that every parameter setting in the discretization results in a tree with exponential expected size, yet there exist parameter settings between the discretized points that result in trees of constant size. In addition, we provide data-dependent guarantees that depend on the volatility of these tree-size functions: our guarantees improve if the tree-size functions can be well approximated by simpler functions. Finally, via experiments, we illustrate that learning an optimal weighting of scoring rules reduces tree size. 

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