Integrating sensory information, such as visual inputs, is critical for many goal-directed behaviors, including motor learning. Motor adaptation, a specific type of adaptive learning where one needs to rapidly learn a new sensorimotor mapping has been an excellent way to behaviorally test how motor learning may be enacted. For example, in humans, perturbing visual feedback results in deviated trajectories that participants can restore to baseline performance over time. Yet, the neural circuits that underlie these visuomotor adaptations remain poorly understood. Here, we developed an implicit visuomotor learning task in a virtual reality (VR) system for mice. Mice are rapidly able to adapt to visuomotor perturbations across trials by modulating their gait and inducing a cross-body asymmetry primarily in their stride length. Given their quadrupedal nature, we also examined nonlinear effects in the kinematics. We used CEBRA on DeepLabCut-extracted limb kinematics and find latent features whose 3D trajectory changes consistently across behavior blocks (across mice), additionally supporting that mice are adapting their limb behavior to counter the perturbation. Taken together, these experiments represent the first evidence of a head-fixed visually-guided motor adaptation in a mouse model, opening the door to probing distributed brain-wide circuits underlying visuomotor adaptation.