International
Twenty one animated films vying for Academy honours
MUMBAI: A record twenty one animated features have been submitted for Oscar consideration.
Computer-animated films continue to dominate, generating big box office as four of the year‘s top 10 highest-grossing films have been computer generated creations. For the first time, stop-motion animation films also had a big year with more than one of the nominees in the feature animation category, being a stop-motion film. Several small time producers at home and overseas served up traditional, hand-drawn fare.
CGI
Since Pixar‘s Toy Story became the first feature-length computer-animated film released way back in 1995, CG has been used to produce a string of animated hits. This year, Disney offered Pixar‘s Brave and Disney Animation‘s Wreck-It Ralph; DreamWorks Animation fielded both Madagascar 3: Europe‘s Most Wanted and Rise of the Guardians which opened on 21 November; and Fox/Blue Sky Studio‘s Ice Age: Continental Drift, Universal/Illumination‘s Dr. Seuss‘ The Lorax and Sony Pictures Animation‘s Hotel Transylvania all hit it big.
Stop-Motion
Among the hopefuls this year are Tim Burton‘s Frankenweenie, which to date has grossed $63.2 million worldwide for Disney. Producer Laika‘s sophomore feature ParaNorman that has collected $97.3 million worldwide is the second film in the category. They are followed by director Peter Lord‘s The Pirates! Band of Misfits.
Hand drawn
This year, contenders include three hand-drawn features from GKIDS Films, the New York-based distributor that crashed the Oscar party one year ago with the surprise animated feature nominations for Chico and Rita and A Cat in Paris, grabbing slots that many expected to go to more high-profile contenders such as Steven Spielberg‘s The Adventures of Tintin.
GKIDS is putting up for consideration From Up on Poppy Hill from Studio Ghibli, The Rabbi‘s Cat, and Zarafa. Rounding up the list of contenders in the category are Walter & Tandoori‘s Christmas and The Mystical Laws.
LOS ANGELES: Acting has never been an easy profession, but in recent years, it has acquired a new existential anxiety. Artificial intelligence can now mimic faces, clone voices and, in theory at least, speak any language it is fed. The fear that actors may soon be replaced by algorithms no longer belongs exclusively to science fiction. And yet, despite the rise of digital inauthenticity, some performers remain stubbornly resistant to replacement. The reason is not celebrity, nor even talent. It is language.
On paper, this should not be a problem. AI can translate. It can imitate accents. It can string together grammatically correct sentences in dozens of languages. But acting, inconveniently, is not about grammatical correctness. It is about meaning, and meaning is where AI still falters.
Machine translation offers a cautionary tale. Google Translate, now powered by neural AI, has improved markedly since its debut in 2006. It can manage menus, emails and airport signage with impressive efficiency. What it struggles with, however, are the moments that matter most: idioms, metaphors, irony, and cultural shorthand. Ask it to translate a joke, a threat disguised as politeness, or a line heavy with emotional subtext, and it begins to unravel. Acting lives precisely in those gaps.
This matters because film language is rarely literal. Scripts, particularly in independent cinema, rely on figurative speech and symbolism to convey what characters cannot say outright. Pedro Almodóvar’s Volver is a useful example. The film’s recurring use of red operates on multiple levels: grief, desire, repression, liberation, and memory. These meanings are inseparable from the Spanish cultural context and emotional cadence. A translation may convey the words, but not the weight they carry. An AI-generated performance might replicate the sound, but not the sense.
This is where multilingual actors gain their edge. Performers such as Penélope Cruz and SofÃa Vergara do not simply switch between languages; they move between cultural logics. Their fluency allows them to inhabit characters without flattening them for international consumption. Language, for them, is not an accessory but a structuring force.
Beyond European cinema, this becomes even more pronounced. Languages such as Hindi, Arabic and Mandarin are spoken by hundreds of millions of people and underpin vast cinematic traditions. As global audiences grow more interconnected, the demand for authenticity increases rather than diminishes. Viewers can tell when a performance has been filtered through approximation. Subtle errors, misplaced emphasis, and an unnatural rhythm break the illusion.
There is also a practical dimension. Multilingualism expands opportunity. SofÃa Vergara has spoken openly about how learning English enabled her to work beyond Colombia and access Hollywood roles. But this movement is not a one-way export of talent into English-speaking cinema. Multilingual actors carry stories, styles and sensibilities back with them, enriching multiple industries at once.
Cinema has always thrived on such hybridity. Denzel Washington’s performances, for instance, draw on the cultural realities of growing up African American in the United States, while also reflecting stylistic influences from classic Hollywood and Westerns. His work demonstrates how identity and influence intersect on screen. Multilingual actors extend this intersection further, embodying multiple cultural frameworks simultaneously.
At times, linguistic authenticity is not merely artistic but ethical. Films that confront historical trauma, such as Schindler’s List, rely on language to anchor their moral seriousness. When Jewish actors perform in German, the choice is not incidental. Language becomes a site of memory and confrontation. It is difficult to imagine an automated voice carrying that responsibility without hollowing it out.
This is why claims that AI heralds the death of language miss the point. Language is not just a delivery system for information. It is a repository of history, humour, power and pain. Fluency is not only about knowing what to say, but when to hesitate, when to understate, and when to let silence do the work. These are not technical problems waiting to be solved; they are human instincts shaped by lived experience.
AI may one day improve its grasp of metaphor and nuance. It may even learn to sound convincing. But acting is not about sounding convincing; it is about being convincing. Until algorithms can acquire memory, cultural inheritance and emotional intuition, multilingual actors will remain irreplaceable. AI may learn to speak. But it cannot yet learn to mean.
In an industry increasingly tempted by shortcuts, language remains stubbornly resistant to automation. And for actors who can move between worlds, linguistic, cultural, and emotional, that resistance is not a weakness, but a quiet, enduring advantage.
