The analysis of audiovisual data aims at extracting high level information, equivalent with the one(s) that can be extracted by a human. It is considered as a fundamental, unsolved (in its general form) problem. Even though the inverse problem, the audiovisual (sound and animation) synthesis, is judged easier than the previous, it remains an unsolved problem. The systematic research on these problems yields solutions that constitute the basis for a great number of continuously developing applications. In this thesis, we examine the two aforementioned fundamental problems. We propose algorithms and models of analysis and synthesis of articulated motion and undulatory (snake) locomotion, using data from video sequences. The goal of this research is the multilevel information extraction from video, like object tracking and activity recognition, and the 3–D animation synthesis in virtual environments based on the results of analysis. An important part of this thesis is dedicated to automatic human motion analysis from video and action/activity recognition. Moreover, we examine the problem of animal animation synthesis using parametric models and algorithms that are based on motion tracking data over appropriate video sequences. The problem of undulatory locomotion analysis led in the definition of a general geometrical problem, the curve EquiPartition (EP). In this thesis, we define, analyze and solve the EP problem. In the case of human motion analysis, we propose a general framework that focuses on automatic individual/multiple people motion-shape analysis and on suitable features extraction, which can be used on action/activity recognition problems under real, dynamical and unconstrained environments. In order to evaluate the robustness of the proposed scheme, we have applied it on various athletic videos from a single uncalibrated, possibly moving camera. The automatic analysis of these videos is a challenging problem due to the complex and fast motions of the athletes and to the unconstrained changes in the environment of athletic meetings. More specifically, an easily expanded hierarchical architecture is proposed, so that a video sequence is classified to video of individual and team sport. Afterwards, the corresponding methods of motion-shape analysis are used recognizing the activity (current sport like high jump, long jump, hurdling, etc.) and the phase (action) at each time (like running, jumping, etc.). Concerning the animals’ animation synthesis, we have examined the problem of animal modeling, 3–D model construction and 3–D animation synthesis in complex 3–D virtual environments. The motion analysis and the 3–D animal model construction are performed using videos captured by a static camera from specific viewpoints. We have proposed distinct methods for articulated and undulatory motion analysis and synthesis. We have applied our articulated motion synthesis methodology for birds (of which we have synthesized an eagle), for reptiles (of which we have synthesized a lizard) and for mammals (of which we have synthesized a goat). The whole methodology can be easily expanded in similar creatures. Concerning the undulatory locomotion, we have proposed an efficient modeling with minimal number of coefficients for noise reduction. Finally, a general planning algorithm on a state graph is proposed for aperiodic and unseen locomotion synthesis. The general geometrical problem of partitioning a continuous curve into N parts with equal chords, under any metric distance, the curve EquiPartition problem (EP), is analyzed and solved. Moreover, we prove that the problem admits at least one solution under the Euclidean distance metric for planar curves, based on an introduced equivalent problem definition, the Level Set Approach. Finally, EP based applications, like polygonal approximation and key frames selection, are presented, and the special properties of their solutions are discussed.