Animation
D23 2026: Every movie and series announced and shown at this years fan event
Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series D23 2026: Every movie and series announced and shown at this year's fan event From'X-Men' bombshells to Pixar sequels, here's what you need to know. Belen Edwards is an Entertainment Reporter at Mashable. She covers movies and TV with a focus on fantasy and science fiction, adaptations, animation, and more nerdy goodness. She is a member of the Critics Choice Association and the Television Critics Association, as well as a Tomatometer-approved critic. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.
Pixars Coco is finally getting a sequel with Coco 2
Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Pixar's Coco is finally getting a sequel with Coco 2 Coco 2 will star a teenage Miguel and feature the return of Ernesto de la Cruz. Coco 2, starring a teenage Miguel and the return of Ernesto de la Cruz, is coming to theaters in 2029. One of the few hit Pixar films without a sequel is finally getting one. At its D23 fan event in Anaheim, Disney unveiled that the company is currently working on a sequel to Coco, tentatively titled Coco 2. This Tweet is currently unavailable. It might be loading or has been removed.
MoCha: Towards Movie-Grade Talking Character Generation
Recent advancements in video generation have achieved impressive motion realism, yet they often overlook character-driven storytelling, a crucial task for automated film, animation generation. We introduce Talking Characters, a more realistic task to generate talking character animations directly from speech and text.
EverybodyDance: Bipartite Graph-Based Identity Correspondence for Multi-Character Animation
Consistent pose-driven character animation has achieved remarkable progress in single-character scenarios. However, extending these advances to multi-character settings is non-trivial, especially when position swap is involved. Beyond mere scaling, the core challenge lies in enforcing correct Identity Correspondence (IC) between characters in reference and generated frames. To address this, we introduce EverybodyDance, a systematic solution targeting IC correctness in multi-character animation. EverybodyDance is built around the Identity Matching Graph (IMG), which models characters in the generated and reference frames as two node sets in a weighted complete bipartite graph.
🎧MOSPA: Human Motion Generation Driven by Spatial Audio
Enabling virtual humans to dynamically and realistically respond to diverse auditory stimuli remains a key challenge in character animation, demanding the integration of perceptual modeling and motion synthesis. Despite its significance, this task remains largely unexplored. Most previous works have primarily focused on mapping modalities like speech, audio, and music to generate human motion. As of yet, these models typically overlook the impact of spatial features encoded in spatial audio signals on human motion. To bridge this gap and enable high-quality modeling of human movements in response to spatial audio, we introduce the first comprehensive Spatial Audio-Driven Human Motion (SAM) dataset, which contains diverse and high-quality spatial audio and motion data. For benchmarking, we develop a simple yet effective diffusion-based generative framework for human MOtion generation driven by SPatial Audio, termed MOSPA, which faithfully captures the relationship between body motion and spatial audio through an effective fusion mechanism. Once trained, MOSPA can generate diverse realistic human motions conditioned on varying spatial audio inputs. We perform a thorough investigation of the proposed dataset and conduct extensive experiments for benchmarking, where our method achieves state-of-the-art performance on this task.
6 science milestones turning 40 this year
In 1986, we had huge leaps forward, tragic steps back, and life changing innovations. NASA's STS-51L crew members pose for photographs during a break in countdown training at the White Room, Launch Complex 39, Pad B. Left to right are Teacher-in-Space payload specialist Sharon Christa McAuliffe; payload specialist Gregory Jarvis; and astronauts Judith A. Resnik, mission specialist; Francis R. (Dick) Scobee, mission commander; Ronald E. McNair, mission specialist; Mike J. Smith, pilot; and Ellison S. Onizuka, mission specialist. Breakthroughs, discoveries, and DIY tips sent every weekday. It was a year that saw roughly six million Americans hold hands in a continuous (more or less) line across the country to raise money for homelessness. A news anchor named Oprah Winfrey debuted her new talk show.
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning. To address this gap, we capitalize on the unique properties of cartoons and construct CausalChaos!, a novel, challenging causal Why-QA dataset built upon the iconic Tom and Jerry cartoon series. Cartoons use the principles of animation that allow animators to create expressive, unambiguous causal relationships between events to form a coherent storyline. Utilizing these properties, along with thought-provoking questions and multi-level answers (answer and detailed causal explanation), our questions involve causal chains that interconnect multiple dynamic interactions between characters and visual scenes. These factors demand models to solve more challenging, yet well-defined causal relationships. We also introduce hard incorrect answer mining, including a causally confusing version that is even more challenging. While models perform well, there is much room for improvement, especially, on open-ended answers. We identify more advanced/explicit causal relationship modeling \& joint modeling of vision and language as the immediate areas for future efforts to focus upon. Along with the other complementary datasets, our new challenging dataset will pave the way for these developments in the field.
The Truth About the em Avatar /em Movies That No One Wants to Accept
James Cameron is desperate to convince the world that these movies aren't "cartoons." Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Sam_Adams newsletter. You can manage your newsletter subscriptions at any time.